SECURE FRAMEWORK FOR IMPLEMENTING DISTRIBUTED …
194
SECURE FRAMEWORK FOR IMPLEMENTING DISTRIBUTED NETWORKED CONTROL SYSTEMS WITH MOBILE OBSERVERS USING HETEROGENEOUS CONTROLLERS BY JAVITT HIGMAR NAHITT PADILLA-FRANCO, B.S., M.S. A dissertation submitted to the Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy, Engineering Specialization in Electrical Engineering New Mexico State University Las Cruces New Mexico December 2017 (c)2017 by Javitt Higmar Nahitt Padilla-Franco
Transcript of SECURE FRAMEWORK FOR IMPLEMENTING DISTRIBUTED …
SECURE FRAMEWORK FOR IMPLEMENTING DISTRIBUTED
NETWORKED CONTROL SYSTEMS WITH MOBILE
OBSERVERS USING HETEROGENEOUS
CONTROLLERS
BY
JAVITT HIGMAR NAHITT PADILLA-FRANCO, B.S., M.S.
A dissertation submitted to the Graduate School
in partial fulfillment of the requirements
for the degree
Doctor of Philosophy, Engineering
Specialization in Electrical Engineering
New Mexico State University
Las Cruces New Mexico
December 2017
(c)2017 by Javitt Higmar Nahitt Padilla-Franco
ProQuest Number: 10759028
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“Secure Framework for Implementing Distributed Networked Control Systems
With Mobile Observers Using Heterogeneous Controllers ” a dissertation prepared
by Javitt Higmar Nahitt Padilla-Franco in partial fulfillment of the requirements
for the degree, Doctor of Philosophy, Engineering, has been approved and accepted
by me following:y m e following: A
(ftvt I/Kts)r. Louf-Vicente Reyes QDr
Dean of the Graduate School
Dr. Nadipuram (Ram) Prasad Chair of the Examining Committee
Date
Committee in charge:
Dr. Nadipuram (Ram) Prasad, Chair
Dr. Jeffrey Beasley
Dr. Jaime Ramirez-Angulo
Dr. Satishkumar J. Ranade
DEDICATION
I dedicate this work to my mother, Lucrecia.
ACKNOWLEDGMENTS
I would like to thank my advisor, Dr. Nadipuram (Ram) Prasad, for giving
me the opportunity to work on his Energy Harvesting project, a true life-changing
experience. Personally, I would like to thank him for sharing his knowledge which
has enriched my study in philosophy. Additionally, I would like to give special
thanks to the next professors of the Klipsch School of Electrical and Computer
Engineering: Dr. Satish Randade, Charles (Chuck) Boehmer, Dr. Xenxin Liu
(Lehigh University), Dr. Paul Furth, Dr. Jaime Ramirez-Angulo, and Dr. Robert
Paz (RIP).
IV
VITA
Jan. 21, 1977 Born in Chihuahua, Chihuahua, Mexico
1994-1999 B.S. Computer Systems EngineeringChihuahua’s Technological Institute II. Chihuahua, Mexico
2000-2001 M.S. Electronics EngineeringChihuahua’s Technological Institute. Chihuahua, Mexico
2002-2003 M.S. Computer EngineeringUniversity of Texas at El Paso. El Paso, Texas
2002-2003 Assistant Instructor and Research AssistantUniversity of Texas at El Paso. El Paso, Texas
2004-present Researcher and ProfessorAutonomous University of Ciudad Juarez. Cd. Juarez, Mexico
2008-2010 M.S. Computer ScienceNew Mexico State University. Las Cruces, New Mexico
2012-2015 Research Assistant and Teaching AssistantNew Mexico State University. Las Cruces, New Mexico
2010-2017 Ph.D. Electrical and Computer EngineeringNew Mexico State University. Las Cruces, New Mexico
PROFESSIONAL AND HONORARY SOCIETIES
Institute of Electrical and Electronics Engineers and Association for Computing Machinery
v
PUBLICATIONS
- Javitt H.N. Padilla Franco, Jorge Perez et al. Control de altitud de un cuadri- coptero utilizando logica difusa. CULCyT/Logica Difusa. 2016.
- Sheret Tilvaldyev, Javitt H.N. Padilla Franco et al. Analysis of the aircraft coating innovations, improving aerodinamics parameters and reduciong sking friction. Academic Journal of Science. 2016.
- Onofre Amador Morfin Garduno, Javitt H.N. Padilla Franco et al. Diseno de un sistema de control descentralizado de un sistema eolico emulado con un grupo motor CD-Generador CA. Coleccion de reportes tecnicos de investigacion. 2015.
- Alejando Monroy Reyes, Javitt H.N. Padilla Franco et al. Sistema de moni- toreo remoto con detecion de movimiento basado en vision por computadora. CULCyT/Sistema de Vision. 2015.
- Octavio Rodriguez, Javitt H.N. Padilla Franco et al. Rediseno, fabricacion e integration de un sistema de vision para mediciones. CULCyT/Metrologia. 2015.
- Luis Alejandro Berumen Carlos, Javitt H.N. Padilla Franco et al. Diseno de her- ramienta para la fabricacion de terminales prototipo. CULCyT/Herramientas. 2015.
- Jose Barajas, Javitt H.N. Padilla Franco et al. Sustainability Via Energy Harvesting and Scavenging. Journal of International Test and Evaluation Association. 2011.
- Rogelio Davila, Javitt H.N. Padilla Franco et a l, Constructive Analysis of In- tensional Phenomena in Natural Language. Journal of Engineering Letters of IAENG. 2007.
- Alberto Ochoa, Javitt H.N. Padilla Franco et al. Explain a Weblog Community. Advances in soft computing.2007
- Alberto Ochoa, Javitt H.N. Padilla Franco et al. Implementing Data Mining to Improve a Game Board Based Cultural Algorithms. Advances in soft computing. 2007.
- Alma Cota, Javitt H. N. Padilla Franco et al. Diseno , Construction y Operation de un Secador Solar de Lodos Generados en Plantas Tratadoras de Aguas. X X X Semana de Energia Solar. 2006.
VI
Javitt H. N. Padilla Franco, Amado Lara, Pedro Marquez. Sistema Descubridor de Conocimiento en Bases de Datos Distribuidas. X X II Congreso Internacional de Ingenieria Electronica “Electro 2001”. 2001.
Javitt H. N. Padilla Franco, Amado Lara, Pedro Marquez. Sistema Inteligente para la Ensenanza de las Matematicas. X X II Congreso Internacional de Ingenieria Electronica “Electro 2001”. 2001.
FIELD OF STUDY Major Field: Instrumentation and Intelligent Networked Control Systems
ABSTRACT
SECURE FRAMEWORK FOR IMPLEMENTING DISTRIBUTED
NETWORKED CONTROL SYSTEMS WITH MOBILE
OBSERVERS USING HETEROGENEOUS
CONTROLLERS
BY
JAVITT HIGMAR NAHITT PADILLA-FRANCO, B.S., M.S.
Doctor of Philosophy, Engineering
Specialization in Electrical Engineering
New Mexico State University
Las Cruces, New Mexico, 2017
Dr. Nadipuram (Ram) Prasad, Chair
In this dissertation, a framework for the development and analysis of low-cost
monitoring and control systems for a set of distributed nodes comprising of dissim
ilar controllers is presented. The ability to connect multiple disparate processors,
is possible due1 to the use of a secure minimalistic custom communication proto
col. This architecture allows the use of mobile devices to interact with the main
features of the system for monitoring and control. The server is at the core the
overall framework, which enables the transparent development of data acquisition,
processing and control models using a software interface. These models automat
ically translate to functional systems executed by all microprocessors available to
the system. The framework contains software libraries that allows the integration
of neural networks and fuzzy logic that permit the implementation of intelligent
data acquisition and control systems.
The random behavior of the underlying communication network is modeled
in real-time to automatically compensate for the delays and lost packages in the
execution of control actions.
The framework enables a mapping of system inputs and outputs to state vari
ables. These variables can then be used to form a system model to enable classical
control theory analysis, such as controllability, observability and stability test.
ix
Contents
LIST OF T A B L E S ..........................................................................................
LIST OF F IG U R E S .......................................................................................
1 INTRODUCTION .................................................................
1.1 Initial D e s ig n ..........................................................................................
1.2 System Requirem ents.............................................................................
1.3 M otivation.................................................................................................
1.4 A ntecedents.............................................................................................
1.4.1 Sensor Networks .......................................................................
1.4.2 Instrumentation of Generators, Hydro-machinery and Wa
ter Flow M easurem ent.............................................................
1.4.3 Gibson’s M e th o d .......................................................................
1.4.4 Wireless Sensor Networks .......................................................
1.4.5 Security in Sensor N etw orks....................................................
1.4.6 Sensor Networks for C o n tro l....................................................
1.4.7 Frameworks for Sensor Networks ..........................................
1.4.8 Energy Consumption in Sensor N etw orks.............................
1.4.9 Stability of Distributed S ystem s.............................................
1.4.10 Mobile Applications for C o n tro l.............................................
1.4.11 Distributed Control Systems, Hybrid S y s te m s ...................
1.4.12 Networked Control S y s te m s .................................................... 11
1.4.13 Stability Analysis and Control of Networked Control Systems 12
1.4.14 Wireless Networked Control S y s te m s .................................... 13
1.4.15 Intelligent Control .................................................................... 13
1.5 Problem D efin itio n ................................................................................ 13
1.6 Objective ................................................................................................ 14
1.7 Hypothesis................................................................................................ 15
1.8 Justifica tion ............................................................................................. 15
1.9 Related W o r k .......................................................................................... 15
2 THEORETICAL F R A M E ................................................................... 17
2.1 Sensor Networks....................................................................................... 18
2.1.1 Instrum entation.......................................................................... 19
2.1.1.1 Instrumentation of Energy Harvesters ................ 20
2.1.1.2 Flow M easurem ent.................................................... 22
2.1.1.3 Signal Conditioning and Sensors .......................... 23
2.1.2 Communication in Sensor N etw orks........................................ 29
2.1.3 Wireless Sensor Networks and Mobile C o m p u tin g 32
2.2 Networked Control S y s te m s ................................................................ 38
2.3 Intelligent Networked Control S y s te m s ............................................ 42
2.4 Mathematical Modeling of Networked Control S y s te m s ................ 47
2.5 Distributed Control in Networked Control Systems ...................... 51
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3 DEVELOPMENT................................................................................... 54
3.1 System Design......................................................................................... 56
3.1.1 Use Case D iagram s.................................................................... 56
3.1.2 Sequence D iag ram s ! . . 60
3.1.3 Class D iag ram s.......................................................................... 65
3.2 Im plem entation...................................................................................... 70
3.2.1 S e r v e r .......................................................................................... 71
3.2.2 Mobile Observer A pplication.................................................... 83
3.2.3 Control N o d e ............................................................................. 88
3.2.3.1 A rd u in o ....................................................................... 92
3.2.3.2 Raspberry P i ...................................... 95
3.2.3.3 National Instruments H ardw are............................. 98
3.3 Deployment and T e s t in g ...................................................................... 102
4 R E SU LT S................................................................................................ 105
4.1 Deployment R esults................................................................................ 108
5 CONCLUSIONS ................................................................................... 109
5.1 Future W ork............................................................................................ I l l
A P P E N D IC E S ................................................................................................. 113
A WATER FLOW M EASUREM ENT................................................... 113
B INSTRUMENTATION IMPLEMENTATION ............................... 125
C EXTERNAL SOFTWARE L IB R A R IE S ......................................... 134
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D SYSTEM PERFORMANCE COMPARISON
REFERENCES ..........................................................
List o f Tables
1 Voltage Scale............................................................................................... 130
2 Simulation Results. [-5mm] .................................................................. 148
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List o f Figures
1 10 KW Hydro Generator........................................................................... 2
2 Monitoring of Multiple Generators......................................................... 3
3 Sensor Network Architecture.................................................................... 18
4 Knowledge Domain Areas Involved in the Project................................ 19
5 Instrumentation Architecture................................................................... 21
6 Sensors with DAQ...................................................................................... 23
7 Accelerometer.............................................................................................. 24
8 Temperature Signal Conditioning Components.................................... 24i
9 Wheatstone Bridge with Thermistor and Amplifier............................ 25
10 Water Proof Pressure Sensor.................................................................... 26
11 Hall Effect Sensor....................................................................................... 27
12 Current Sensor............................................................................................ 28
13 Resistive Divider for Voltage Measurement........................................... 28
14 Instrumentation GUI................................................................................. 29
15 Communication Sequence......................................................................... 30
16 Communication Protocol.......................................................................... 31
17 Distributed Monitoring.............................................................................. 33
18 Wireless Sensor Network Implementation............................................. 34
19 Security and Multiple Clients................................................................... 35
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Communication Protocol with User Authentication..........................
Software Architecture...............................................................................
Mobile Application Graphical User Interface......................................
Elephant Butte Irrigation Channel Drop 8..........................................
Control Nodes with Communication.....................................................
Mathematical Model Incorporating Real Inputs and Outputs. . .
Simple Mathematical Model of a Neuron.............................................I
Simple Local Intelligent Control on Node a Based on Artificial Neu
ral Networks...............................................................................................
Fuzzy Sets Representing a Particular Rule..........................................
Control Node Implementing a Fuzzy Controller.................................
Discrete and Continuous Elements of a Control Node......................
Hybrid System...........................................................................................
Simulation of the Hybrid system of Figure 31....................................
General Architecture................................................................................
UML Use Case Diagram for Control Node..........................................
UML, Use Case Diagram for the Math Module...................................
UML Use Case Diagram for the Server................................................
UML Use Case Diagram for the Mobile Client...................................
UML, Sequence Diagram for Reading Data from a Mobile Observer.
UML Sequence Diagram for Autonomous Control.............................
xvi
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UML Class Diagram for the Control Node.
UML Class Diagram for the Math Package.
UML Class Diagram for the Server Package
UML Class Diagram for the Client Package.
Server Login GUI............................................
Server System Preview Login GUI..............
Screen Detail of the System Preview GUI.
Server Data Layer Node Selection GUI. . .
Data of Selected Node GUI...........................
Link Selection GUI.........................................
Network Analysis GUI...................................
Control Design GUI........................................
Stability Analysis GUI...................................
Custom Node Specification GUI..................
Login Activity..................................................
Application Main Activity.............................
Server Access Services Activities.................
Activity Node Observation and Control. .
Instrumentation Activities............................
Manual Control Outputs. ..........................
Generic Control Node Algorithm.................
xvii
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Arduino
Arduino implementation of Open CACE Protocol. . . .
Raspberry Pi 3 model B.......................................................
Raspberry Pi Output Plate from PiPlates.......................
Raspberry Pi and Arduino..................................................
Wifi USB Dongle...................................................................
Python Implementation of Open CACE Protocol. . . .
NI USB Devices.....................................................................
NI CompactRIO....................................................................
Protocol Header in LabVIEW.............................................
Header TCP Transmission in LabVIEW..........................
Final Knowledge Domain Areas Involved in the Project
Gap Between Models and Implementations.....................
Contraction Coefficient.........................................................
Pipeline Cross Section..........................................................
Schematic Signal Conditioning PCB..................................
Final PCB for Signal Conditioning....................................
Linearized Pressure Sensor Response................................
Linearized Accelerometer Response...................................
Linearized Voltage Response Nom-High Swing...............
Linearized Voltage Response Nom-Low Swing................
xviii
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Linearized Current Response.................................................................. 132
LabVIEW Instrumentation Code........................................................... 133
Lighthouses in the East and West Coast.............................................. 137
Control of Lighthouse Mirror Motor..................................................... 138
Simple Distributed Control..................................................................... 140
LabVIEW DCAF Configuration............................................................ 142
DCAF Main Test Module....................... -............................................... 143
Mathematical Model of the Lighthouse Problem................................ 144
Open CACE implementation of the Lighthouse Problem................. 145
LabVIEW Simulation of the Lighthouse Motor Distributed Control. 146
Open CACE Simulation of the Lighthouse Motor Distributed Control. 147
Test Execution Comparison.................................................................... 149
xix
1 IN TR O D U C TIO N
Energy storage and electricity production stand out as the fundamental build
ing block of today’s digitally oriented world. Coal, gas, solar cells or water turbines
must be utilized to generate electrical power. During the spring of 2011 research
was conducted by Dr. Nadipuram (Ram) Prasad to harvest energy from water
flows. That work was funded by the Department of Energy[39] due to its origi
nality and novel approach in using carbon composite materials such as Kevlar to
fabricate the turbine components.
The idea was to construct several low-cost, self-contained generators. An extra
feature of these energy harvesters was to provide real-time data on their behavior.
The research topic of this dissertation is the result of the author’s work on the
instrumentation and control of the hydro generators.
Figure l.a shows a diagram of the hydro generator’s modular architecture. Its
two main components can be observed: generator housing and discharge elbow.
The 10 KW generator resides inside a sealed submarine, which can maintain a
water-tight environment. As Figure l.b shows, the submarine is attached to the
venturi-shaped housing by the means of two hollow columns. The inside of the
columns is designed to hold the instrumentation circuits. Also, the power and
interface cables are directed to the exterior using the empty space in the columns.
The power cable transmits the generated power to the load, and the interface
(b) Components.(a) Modular Design.
(c) Impeller. (d) Final Assembly.
Figure 1: 10 KW Hydro Generator.
cables carries all sensor signals to the monitoring console. Figure l.c shows how
the aluminum impeller is mounted inside the turbine. After assembling the turbine
on the top, and the discharge elbow on the bottom, the structure stands more
than two meters tall, as illustrated in Figure l.d for an scale idea. An advantage
of the modular design is the ease of deployment. The modular components can
be transported to the installation site and assembled quickly. Also, the additive
manufacturing process allows the construction, and installation of custom-made
2
housing suitable for site specific requirements.
A team consisting of doctoral, master, and undergraduate students worked
diligently under Dr. Prasad’s direction to achieve the system of Figure 1. A group
of students worked on the development and integration of the technology required
to measure and display the generator’s physical variables such as speed, vibration,
temperature, water flow and generated power. One important characteristic of
the design is the ability to deploy many units to generate additional combined
power. Thereifore, the instrumentation must support the connection of multiple
generators, see Figure 2.
Monitoring and ■: Control: System
Generator Generator,
Figure 2: Monitoring of Multiple Generators.
1.1 Initial Design
The instrumentation and control of two 10 KW hydropower generators is
a challenging task. It involves: i) Establishing a communication network, ii)
Ensuring this network is secure, iii) Message coordination between components
to achieve a global state, iv) Display measured values to the user, v) Enable the
easy adjustment of control outputs by the user.
It is common in a rapid prototyping environment to select National Instru
ments (NI) components due to its availability and reliability. However, such a
decision makes it a high-cost implementation. The objective is to develop a low
cost and reliable instrumentation of the system. A low-cost structure can be
realized with the use of Arduino and Raspberry Pi micro-controllers.
Arduino and Raspberry Pi can interact with different sensors. The instrumen
tation was designed to measure water flow passing through the turbine, as well as
the voltage and current generated. Those measurements could be used to monitor
the system behavior or to control system state variables such as the water inlet
gates or the angle position of the impeller.
The next step after the selection of the acquisition devices is the communica
tion with LabVIEW software. Communication is required to transmit the mea
surements from the sensor nodes to the instrumentation Graphical User Interface
(GUI) in order to be displayed. The first implementation of this GUI was pro
grammed in the NI environment because of its multiple visualizing components.
LabVIEW works flawlessly with NI devices, but for Arduino, and Raspberry Pi
additional software must be used to enable interaction with the NI environment.
Some libraries exist to communicate between Arduino, and Raspberry Pi with
NI software. Unfortunately, they do not offer the simplicity required for efficient
communication between microcontrollers. Thus, regular serial or socket commu-
nication was utilized to perform remote measurements and then send them to the
monitor GUI.
1.2 System Requirem ents
Several requirements were imposed on the design. The main requirement
was that the systems must be scalable. The plan was to have many generators
interconnected, such that every one of them can be monitored. In the first itera
tion of the project, the measurement system was supposed to fulfill the following
requirements:
• It shall measure and record generator’s running variables, such as inlet water
pressure, outlet water pressure, generator vibration (in three axis), chassis
vibration (in three axis), water temperature, generator temperature, gener
ated voltage and current.
• It shall compute the water flow using the calculated water velocity from the
differential water pressure measurements and the pipe diameter.
• It shall display the sensor readings on a monitor, in a user-friendly interface.
• It should be a low-cost reliable implementation.
I• It shall provide an infrastructure to monitor several generators concurrently.
5
During the project evolution, additional requirements were added in response
to the research maturation. Those additional system qualifications are:
• It shall be able to provide control outputs in each sensor node.
• It shall support Wireless communication using standard Internet Network
Protocols.
• It shall allow the remote monitoring and control using mobile devices.
I
• It shall offer a GUI that allows the Control Engineer (CE), the user, toi
program and modify user-defined control mechanism easily.
• It shall be secure in its communications among components, i.e., no unau
thorized people should monitor or control the system.
1.3 M otivation
During the initial research for the design, no single tool that combines hard
ware and software able to cover the whole set of requirements was available. Such
a system could be implemented using Lab VIEW but with considerable amount of
effort. Even though this software tool in conjunction with its supporting hardware
provide many of the features required for the implementation, it is very expensive,
and it can not be called low-cost. Also, it lacks heterogeneous hardware support.
Additionally, Lab VIEW does not consider the delays in the network for the design
6
of distributed control. This situation represents an excellent opportunity to fill
this market-gap by creating a reusable framework that allows the interaction of
many heterogeneous components to form a networked control system. This dis
sertation in addition to describing the design and implementation process of such
system, also goes into the formal analysis of the networked control systems in
order to create a theoretical model with attributes, such a network statistics and
controller dynamics. This model is then implemented into a software tool which
translates and executes its representation into a distributed environment made of
heterogeneous components.
1.4 A ntecedents
Several research papers were analyzed during this project development, and
the main results are briefly presented and classified in the next section. Research
topics are chronologically ordered according to the time they appeared on the
project.
1.4.1 Sensor Networks
This project started as an instrumentation of hydro energy harvesters, in
which every harvester will contain a set of sensors. Those harvesters are inter
connected using a computer network. In the electrical engineering field, the area
of “Sensor Networks” deals with the design and theoretical aspects of sensors in-
terconnected by a network topology. Some topics were considered relevant during
the initial stages of the research like the ability of the sensing nodes to perform
some distributed computation [120]. The design of sensor networks often uses a
layered architecture [170] to achieve tasks such as data aggregation [76], real-time
communication [150, 183], better querying [33] and ease of management [40].
1.4.2 Instrum entation of Generators, Hydro-m achinery and W ater Flow
M easurem entiI
After defining the sensor network, it is necessary to establish how to measure
the physical variables. The principal elements to instrument in the case of the
hydro-generator are: water flow, voltage, generated current and vibration. Some
authors address measurement techniques in hydroelectric generators like [153, 144,
100, 104, 72]; other authors do the same for wind and steam turbines [142, 176].
Data obtained from the instrumentation is the used to mathematically model
behavior of generators as in [167, 66, 151, 108, 123]. This data could also be used
to present on-line monitored data to users [15]. Design Testing can be performed
using real-time instrumentation [64]. One example of such testing is blade pressure
analysis [8]. Some studies had been realized over the vibration of generators
[37, 32], One of the primary goals of generator instrumentation is to calculate its
power efficiency [166, 111].
8
1.4.3 G ibson’s M ethod
Flow measurement is a key component in the instrumentation therefore it is
widely studied [177], especially in the case of flow inside pipelines [3, 69, 2]. An
integration technique called the Gibson’s Method is normally used [163, 70, 69, 2,
71] with differential pressure sensors on pipelines.
1.4.4 W ireless Sensor Networks
It is possible to extend the concept of a sensor network with the use of the
wireless technology. Some issues are treated in the literature [184, 141, 96] as
data aggregation of multiple nodes [1]. Several problems can be solved in wireless
sensor networks by developing a custom protocol [31, 55]. Some mathematical
models for the algorithms for wireless sensor networks [78] were studied. Multiple
areas could be involved in sensor networks as explained by [155]. Other problems
that will arise are coordination [24], coverage [63], load repartition [95]. Some
examples of other wireless sensor network projects were studied with application
in different domains and environments [139, 116].
1.4.5 Security in Sensor Networks
Sensor data travels from node to node inside a network of sensors. The
internetworking of its design, make sensor networks vulnerable to attacks. Thus,
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security is an important aspect of communication design in sensor networks [172,
112]. Wireless sensor networks [35, 113] present different challenges.
1.4.6 Sensor Networks for Control
Sensor networks could perform some control actions at the node level [148, 90].
Some extrapolation of the classical control theory to sensor networks [94] is desir
able. Artificial intelligence and intelligent control [51, 147] is used in conjunction
with sensor networks, and even game theory [135] is applied.
1.4.7 Frameworks for Sensor Networks
One common design characteristic in sensor networks is the utilization of an
extra layer [98] to mask heterogeneity [115]. Other aspects such as communication
[159] or coordination aspects [20] inside sensor networks are implemented in that
additional layer. This extra layer is usually referred as “middle-ware” [58] or a
“framework” [61, 73]. Those frameworks are used with wireless technologies as
well [89, 117, 30],
1.4.8 Energy Consum ption in Sensor Networks
From the design phase, the energy consumption of the sensing nodes on the
sensor network is important. Protocols [121], frameworks [73], topologies [129, 88],
power-aware algorithms [185], game theory [135] and, other techniques [5] are used
10
to achieve optimal power consumption.
1.4.9 Stability o f D istributed System s
Research was conducted on the stability of distributed systems such as a
Nyquist-type stability test for multi-variable distributed systems [92] and how to
maintain stability in a distributed generation system [25].
1.4.10 M obile Applications for Control
Mobile computing refers to the use of cellular telephones and the development
of their applications. The use of mobile computing in automatic control [6, 19, 105]
allows the ubiquitous access to system variables to change their values.i
1.4.11 D istributed Control System s, Hybrid System s
Distributed control is when components to be controlled reside in separate
geographic locations. Therefore this project perfectly fits into the definition of a
distributed control system [44, 161, 87, 9, 138]. A distributed control system can
be mathematically modeled by a hybrid system [21, 93, 22].
1.4.12 Networked Control System s
The proper subset of distributed control systems that represents the project
is the “Networked Control Systems” [60]. These systems have their mathemati
11
cal analysis and models [86], but are extremely dependent on the communication
[109]. Implementations of classic Proportional-Integral-Derivative (PID) in wire
less environments [162] and other aspects such as behavior algorithms [106] are
examples of research in this area. Communication models [47] and communication
logistics [181] are also studied. Security remains a central topic due to the nature
of the system wireless construction [154, 190, 157, 156].
1.4.13 Stability Analysis and Control of Networked Control System s
Some papers go a little bit deeper in the stability analysis and control of Net
worked Control Systems [168]. Others use Markov models to model the random
delays in the network [140]. State Feedback can be used [189, 186] to control the
system. Others do a characterization of input-output stability [102], There are
stochastic hybrid systems [10] also. Stabilization of a system when the packets
can be dropped randomly [188, 178], random delays [46, 28] or random sampling
periods [84] and their challenges were contemplated. Other stabilization topics
that were studied include adaptive stabilization [152], symbolic control design
[18], Communication Constraints [57] and, optimal control under communication
constraints [45]. Techniques such as event-triggering in Distributed Networked
Control Systems [174] and its impact on bandwidth in a wireless network [173]
were also examined.
12
1.4.14 W ireless Networked Control System s
The logical research evolution is to include wireless into the game creating
a Wireless Control Network [110]. Other authors considered and studied the
implementation requirements for wireless networked control systems [99] and their
models [62].
1.4.15 Intelligent Control
Artificial Intelligence refers to the use of mathematical models that resemble
to some extent the abilities of the human mind. Computational intelligence could
be used to achieve intelligent control of normal [192] and distributed systems
[175, 179, 145, 191, 107],
1.5 Problem Definition
In the course of research conducted for the development of the instrumen
tation system, numerous theoretical models were examined to describe the be
havior of networked control systems. Also, various implementation frameworks
were considered to build the actual system. However, no single approach allows
the theoretical side advantages, such as the system’s proof of stability, and the
pragmatical side advantages, like the ease of deployment or security.
The central problem addressed by this dissertation is the necessity of merging
13
theory and practice into one suitable framework for the design, analysis, develop
ment, and. deployment of networked control systems.
1.6 O bjective
The main objective is to build a software infrastructure that allows the con
trol and monitoring of multiple heterogeneous controllers distributed among the
Internet. This infrastructure must have the following specific objectives:
• It shall provide a secure schema to ensure that only authorized users will
monitor and control the nodes in the system. Also, the communication
between nodes must remain private at all times.
• It shall allow the user to perform a static analysis on the system such as
multi-variable Nyquist-type stability test.
• It shall allow dynamic analysis by recording activity of the system’s com
munication. That information shall be used to model the random network
behavior using a hidden Markov chain. This model shall be utilized by the
framework to compensate for random delays on the communication.
• Given a 1 mathematical model to the framework, this shall automatically
generate a suitable mapping of the hardware components to implement the
given architecture using the available components efficiently.
14
1.7 H ypothesis
The main hypothesis is that by the creation of a robust enough infrastructure
the implementation, and analysis of distributed control systems will become a
simple interaction with a programmers interface. Also, if the implementation
supports the interaction with heterogeneous components then its cost could be
reduced. Finally, if the infrastructure uses standard mathematical nomenclature
for describing its inputs and outputs, then common mathematical procedures for
control could be easily applied to the distributed system.
1.8 Justification
It is important to have a software infrastructure that flawlessly mixes the
world of control system synthesis with the real world implementation. The work
here presented fills this important gap in control engineering.
1.9 R elated Work
Many tools try to prove some mathematical properties out of hybrid systems.
Those conclusions mainly are if a particular state is reachable from an initial con
dition. Some examples are mentioned next, but in no particular order. PH AVer
is a Hybrid systems verifier [42]. The tool named d\dt was designed to perform
reachability analysis of hybrid systems [11]. HyTech is used establish the condi
15
tions under a hybrid system satisfies a constraint [59]. CheckMate is a tool for
modeling, simulating and verifying properties of hybrid dynamic systems [143].
Matisse is a verification and reachable set computation of linear systems [49].
Ellipsoids is a library in MATLAB that can be used for reachability calculations
[80]. SpaceEx is a tool to verify, i.e., compute the sets of system’s reachable states
given a mathematical model of a hybrid system [43]. UPPAAL, It can model
and validate real-time systems that have been described as networks of timed au
tomata [13]. HSolver is a verification tool for Nonlinear Hybrid Systems [124].
KeYmaera is a Hybrid verification and theorem Prover for Hybrid Systems [114].
PowerDEVS is a general-purpose software tool for Discrete Event System specifi
cation modeling and simulation used to simulate hybrid systems [14]. HyEQ is a
Hybrid System Solver for MATLAB [128]. HyCreate is a tool for approximating
reachability of Hybrid Automata [12].
All these tools cover the mathematical modeling, simulation, and validation of
hybrid systems. They are all related to this dissertation in their effort to compute
trajectories of hybrid systems. Nevertheless, they do not allow the extrapolation
of the models to the real world application. For the real world construction of
networked control systems, LabVIEW reigns as one of the industry standards due
to the ease of its visual programming environment.
16
2 THEORETICAL FR AM E
This chapter wili briefly cover the theoretical foundation studied to produce
this dissertation. Topics are discussed as they appeared on the project. The
approach of this section is to describe the application of the knowledge to the
project itself.
At first, when presented with the challenge of instrumenting and controlling
a set of hydro harvesters, the first thought was about the sensor data commu
nication to the monitor station, i.e., how the sensor data flows from the node
to the terminal. Another crucial aspect to consider was that the system had to
be implemented in real life at a reasonable cost, i.e., research papers usually as
sume theoretical situations that tend to be hard or expensive to integrate, in a
congruent manner and meeting the established requirements, using top-of-shelf
components. Lastly, it was imperative to create a mathematical model from the
deployed system. This model shall be used to characterize the system’s function
ally, and then some stability could be proved. Additionally, the model shall allow
the development of a global optimum controller. First its optimality should be
theoretical established wit the help of the system’s mathematical libraries. This
model is then translated automatically to be executed on the array of nodes.
Communication seemed at the beginning to be the main issue, so we now
introduce some concepts in the sensor networks.
17
2.1 Sensor Networks
A sensor network consists of a set of sensors interconnected to a main ob
serving point. Figure 3 shows an architectonic view of a sensor network, many
crucial aspects of the practical implementation are omitted in this diagram. The
main topology could be observed, i.e., lines represent transmission media, but it
is not clear how communication is achieved. During the initial design phase, one
clear pattern emerged in sensor networks: the academic papers differ extensively
from the implemented technologies available commercially. Usually to construct
a sensor network, a company is hired to provide all the infrastructure tailoring
general products, like NI. On the other hand, research covers topics from physical
layer protocols, moving upwards in complexity all the way to the user application.
Research is seldom related to actual companies and its almost always independent
Sensor Node Gateway Sensor Node
Figure 3: Sensor Network Architecture.
18
from particular devices. With this in consideration, the first design was exten
sively based on commercial components. The knowledge domains involved in the
initial design put the project at the intersection of computer networks and instru
mentation, as represented in Figure 4. Computer networks were introduced to
the game due to the fact that remote monitoring, security and mobile computing
were in the requirements. In a nutshell, the project was a sensor network that
consists of instrumentation nodes interconnected through a network.
U
Sensor Networks
Computer /J
Networks 1
Instrumentation
Figure 4: Knowledge Domain Areas Involved in the Project.
2.1.1 Instrum entation
This section briefly introduces the theoretical concepts used in the instrumen
tation of the hydro energy harvesters. This instrumentation consists of a series
of sensors monitoring the behavior of the different components installed on the
19
generator. Those sensors are then connected to a virtual instrument console that
allows easy interaction with the user.
2.1.1.1 Instrum entation of Energy Harvesters
It is necessary to define which physical phenomena shall be measured, the
most important variables to monitor from a hydropower generating system are:
• The flow of water through the generating module. Q usually represents flow
3rate, and it is expressed in cubic meters per second
• Generated Voltage and Current (with their respective frequency). The volt
age is proportional to the water flow passing through the generator.
• Water temperature. Different temperatures will affect the water density
thus changing the system behavior.
• Generator temperature. This measurement is important to ensure the safe
operation of the generator by avoiding overheating.
• Vibration. Unusual movement could be a sign of malfunction inside thei
generator.
• Shaft Speed. In combination with the water flow, this value is used to
calculate the generator’s efficiency.
20
In Figure 5 the instrumentation architecture diagram shows that in order to
read the physical measurements, an interface is needed to acquire and communi
cate data to the user. This real-world interface is called Data Acquisition (DAQ)
\= (£ M S S
/ — \ Wh
MonitoringConsole
DAQ Hydro Generator/ Sensors
Figure 5: Instrumentation Architecture.
system. One big drawback of such devices is their high price. Following the de
sign requirement of constructing a low-price device, the Arduino was selected as
DAQ because; of its low price and twelve I/O analog channels, with 9615 Hz of
maximum sampling speed. The selection of the sampling speed obeys some tech
nical reasons related to the rate of the acquired signals such as: i) The expected
maximum shaft speed is around 300 RPM. ii) The generated voltage shall have a
frequency in the range of 60 Hz. Other signals, even though time-dependent, are
not critical for the instrumentation; thus they can be monitored at any available
sampling-rate.
21
2.1 .1 .2 Flow M easurem ent
For the proper generator characterization, it is important to estimate how
much water is flowing through the system and its response to the passing flow in
terms of electrical generation. See Appendix A. 1 for a brief introduction to fluid
dynamics.
The water discharge Q over a pipeline with cross-sections areas of A\ and A 2
and at different heights hi and h2 is:
Q = C o A ,A 2] f ^ A f (1)
where Cc represents the contraction coefficient.
Many standard methods are applied to measure the volume of water passing
a determined area. For this project, the differential-pressure flow meter was used,
i.e., Q must be estimated using knowledge about the dimensions of the pipeline
and the pressure the water is applying on the walls of the pipe. Consult Appendix
A.2 for a brief explication of differential pressure flow-meters.
Equation 1 can be computed using differential pressure as follows:
• 1 i 2 ■ s / m - P*)pi (2)P2 ^
with /3 = Appendices A.3 and A.4 present a detailed derivation of Equations
1 and 2.
The implication of Equation 2 for the instrumentation is that water discharge
22
Qm
shall be computed using two pressure sensors placed on different segments of the
pipeline feeding the generator. Additionally, other sensors are required to instru
ment the generator. In Figure 6 a general architecture of the sensor connection is
displayed.
5 1 E S B 8 2 1 1 1
©©(Effil)
<-------------Voltage ■ <-------------Current ■<— Generator Temperature —> < Water Temperature > <------- Vibration (x , y, z ) ------ > <----------- Shaft Speed----------- >
<---------- Inlet Pressure - <---------Outlet Pressure ■
Figure 6: Sensors with DAQ.
2.1.1.3 Signal Conditioning and Sensors
All sensors considered for this project react differently to the environment.
The acceleration sensor changes in voltage when moved, while the pressure one
will change its resistance. The DAQ internally has an analog-to-digital converter
with a specific voltage and current range.
Figure 7 shows the ADXL335 acceleration sensor from Analog Devices used
on this project. The acceleration sensor provides three outputs, one for each axis.
Each axis has an output voltage between -3.3 and 3.3 volts.
23
mRID|B
SP
Its range of operation is ±5 gravi
ties. Scaling was performed on soft
ware with a linear function approxi
mated by experimental data from the
initial testing of the chip. See Ap
pendix B.2 for more details on the scal
ing.
Sensing temperature requires additional steps. For this project, the thermistor
Figure 7: Accelerometer.
of Figure 8b was used. To map the change in resistance to change in voltage, the
circuit of Figure 9 was implemented. Scaling was partially achieved by using the
instrumentation amplifier of Figure 8a.
AD8221TOP VIEW
(a) AD8221 Precision Instrumen- (b) YSI-55008 Precision Thermis-
tation Amplifier, Analog Devices, tor 30K fh
Figure 8: Temperature Signal Conditioning Components.
24
The final part of scaling was done by software in order to neglect the non-
linearities that the thermistor presents at its limits of operation, see Appendix
B.2 for exact scaling equations.
9Vcc
out
Figure 9: Wheatstone Bridge with Thermistor and Amplifier.
For the circuit in Figure 9, the input-output relation is the next. First, it was
established that the voltage at the non-inverting terminal is:
R tV+ = Vcc
R x + R T (3)
For the inverting terminal, KCL can be applied,
- V- Vcc - V_ V-R R'2 R ‘A (4)
Since for an ideal Op Amp, the voltages at the inverting and non-inverting ter
minals are the same. VL = V+ can be plugged to Equation 4. After rearranging
25
like terms:
Vout = RVcc Rr ( 1 1 1 \ 1+ ^ - + (5)Ri + Rt \R2 R$ R ) R2
The mapping described by Equation 5 will provide the input voltage for the scaling
process. That will convert with a linear approximation the provided voltage into
usable temperature. The scaled range of action is from 0 to 60 Celsius degrees.
To measure pressure, the water-resistant package shown in Figure 10 was used.
The unit contains a sensor, Wheat
stone bridge and amplifier encapsu
lated and hermetically sealed. For
this sensor, the scale is entirely im
plemented in software. The package
needs a input voltage of 5 volts and its
ground. The sensor generates output Figure 10: Water Proof Pressure Sensor,
voltage proportional to the pressure on
the tip. Its range is from 0 to 5 volts scaling accordingly to 0 to 33 PSI. The sen
sor model US333 could be bought from h ttp ://w w w .s tra in se n se . c o .uk/ with a
price of 165 USD. Its output final scaling was approximated using a linear function
also obtained from the initial device testing.
Temperature and pressure are required to compute flow as described before.
The instrumentation of this project also requires the ability to calculate efficiency,
both mechanical and electrical.
26
Using a Hall effect sensor US 1881 from Melexis, see Figure 11, in combination
with magnets attached to the shaft of the generator, it is possible to generate and
measure an analog signal with peaks on every magnet pass.
Once that signal is acquired, a software
processing is performed in order to
compute its frequency. That frequency
is relative to the number of magnets
present on the shaft and therefore the
5V+_________ 3 T I ________digital--------------1--------- I I OUT
G N D "------------ 1----------------1 *p J.,- rI Hff I
speed of the shaft. The software ap- Figure 11. Hall Effect Sensor,
proach was to use a digital counter over
a finite frame time, and since the number of magnets remains constant, it is just
a division to obtain the frequency, see Lab VIEW code on Figure 83 in Appendix
B.3.
For the current measurement, the Integrated Circuit ACS756 from Allegro,
see Figure 12, was used. This IC gives a voltage that is linearly proportional to
the current passing through the sensing input pins. Once the voltage reading is
acquired, scaling is performed with software by transforming the reading from a
range of 0 to 5 volts into 0 to 100 Ampere.
To measure generated voltage and since it is an AC generator, a transformer
was used to step down the generated voltage so it could be acquired by the DAQ.
27
For an additional scaling, a voltage di
vider was connected as shown in Figure
13. Several measurement types could
be implemented with this configura
tion, such as phase-to-phase, or phase-
to-ground. Once the measured signal
was acquired, software scales back up
the reading into the actual voltage. This scale considers the transformer’s linear
ity and the resistance values of the voltage divider. A printed circuit board was
Figure 12: Current Sensor.
Vin. Vi
V i n _
out
Figure 13: Resistive Divider for Voltage Measurement.
designed to hold all the signal conditioning, see Appendix B.l. In order to present
the measured values to the generator operator, a Graphical User Interface (GUI)
in Lab VIEW was designed. In Figure 14 the acquired variables are displayed using
graphical components. This GUI also allows the data logging of the generator’s
2 8
operation. The program that controls the GUI is shown in Figure 83
EI3DO NMU.S. OF
ENERGYSTATE Energy Efficiency &
Renewable Energy
11*41
pm
_:'yz 1
**lr r w
Figure 14: Instrumentation GUI.
2.1.2 Com m unication in Sensor Networks
Communication inside the sensor network is the most important aspect of its
implementation. Communication could be expressed as the sequence of messages
between the participants and the structure or format these messages have. One
of the key requirements was to have a low-cost implementation, Arduinos were
used initially as the DAQ devices. Arduinos have a shield that implements the
Ethernet hardware, and thus the communication could be greatly simplified by
the use of standard transport protocols such as TCP/IP. Due to Arduino’s very
limited hardware, it was required to program the communication at the lowest
29
level, i.e., using the socket interface.
The most basic form of interaction occurs when the server (Control GUI) re
quests data to be displayed on the screen. Instead of the sensor node transmitting
every single change on the sensor, which will produce network congestion, the
server will request data from the sensor node every time an update event occurs.
In Figure 15 the messages required to perform this operation are shown. First, the
server, which was waiting for some event such as data refresh, send messages to
the read the x sensor. Upon arrival, the sensor node submits an acknowledgment
to the server and starts gathering the required data from the sensor. Once the
data is collected the sensor node transmits it to the server. If the server got the
requested data, it would send an acknowledgment to the sensor node.
Sensor Server Sensor NodeWAITING
display variable
SENSOR_X_READsensor data
_READ OATA nr.Y'n esw si REQ_RECEIVEDpattering sen sor data
SENSOR DATA.SENDREQ_ACCEPTED
READING .DATA
CLOSED
Figure 15: Communication Sequence.
30
For the message, a custom protocol is defined for the transmission of acquired
data. Figure 16 shows the frame that travels inside a TCP frame. The fist 16 bits
represent the TYPE of the message. It is intended that the protocol will support
many operations, such as update variables or read values from the sensors. Such
different TYPEs of messages will be encoded on these bits. The next 4 bits will
describe the VERsion of the protocol used, and 12 bits of optional FLAGS. For
the source node 16 bits describe the requester numerically , and another 16 bits
describe the node that will provide the data. TYPE is the encoding requested.
NUMPACKS is the number of readings requested. The sequence number is con
secutive integer assigned to the package. A time stamp will contain the operation
time. The payload will contain N sensor reads each one with an ID.
X
He
er
Pay
> load
Figure 16: Communication Protocol.
PACKID N SENSOR DATA N
DATA N cont’d . . . optional padding
0 7 11 15 19 31
TYPE VERS FLAGS
source node destination node
sensor number TYPE NUMPACKS sequence number
timestamp
PACKID 1 SENSOR DATA 1
DATA 1 cont’d • • •
31
Future implementations of the system will allow the porting of protocols used
in the electric industry such as the DNP3 (Distributed Network Protocol) and the
IEC 60870.5, a good reference source on these protocols is [27].
2.1.3 W ireless Sensor Networks and M obile Com puting
One disadvantage of the sensor network is the requirement of a guided medium
to establish the communication. The portable design of the energy harvester
allows to be deployed quickly throughout many different operation environments.
Such locations could not always provide wire connectivity to the Internet. Wireless
sensor networks provide a theoretical frame to expand on the sensor networks.
Also, they introduce the ability to create mobile interfaces to display sensor data,
since cellular telephones could be easily integrated. In Figure 17 a scenario is
presented in which many harvesters have been deployed on the irrigation channels
of the state of New Mexico; all of them are interconnected using wireless adapters
to the Internet. The system could then be monitored using a mobile device such
a tablet or cell phone.
To achieve such behavior the architecture of Figure 5 must include the WIFI
Arduino’s shield. Also, some access point must be considered. In Figure 18 a
router is used, to integrate the wireless sensor network to the Internet. Mobile
devices are used then to monitor the instrumentation variables in real time.
One big challenge that the wireless sensor networks confront is the security.
32
MobileDevice
Internet
GeneratorArray
Figure 17: Distributed Monitoring.
On the architecture of Figure 18, there is no way to validate who is accessing the
wireless sensor network unless you hardwired them to the sensor nodes and the
mobile devices, i.e., the sensor network could only send data to a list of particular
preprogrammed mobile devices. Any change will require a recompilation and
redeployment of the sensor node code. Figure 19 adds an access server to deal
with the security and registration of mobile devices, and as well to receive and log
all the sensor activity.
By adding the access server, the operation of the wireless sensor network is
changed. In Figure 20 an extension of the original protocol is described in which
the Access server is included as well as the mobile device. The main change is that
33
MobileDevice
WirelessRouter
Arduino + WiFly „ S!gnal. Hydro Generator/Conditioning Sensors
Figure 18: Wireless Sensor Network Implementation.
user must be authenticated to read the data acquired. After the user is validated,
the transmission of the sensor data occurs using the sequence of Figure 15. Now
the server will transmit the data to the mobile client. By having an access server
not only security is improved, but also this server could be permanently monitoring
and logging all sensor nodes, or even it could be programmed to perform some
automatic scripts affecting the whole sensor network.
The wireless sensor network with mobile observers is constituted by three parts:i
mobile device, access server, and the energy harvester. To be a useful monitoring
system, it must allow many mobile devices to access the network simultaneously;
also many harvesters are part of the network. To provide a reliable service, the
system must have many access servers to ensure the proper functionality on net
works consisting of many clients. Even though there are many components in
the wireless sensor network, all mobile devices run the same program, the same
happens to the servers and harvesters. Figure 21 shows the software architecture
of all the components of the sensor networks.
35
Mobile Device Server Sensor Node
Figure 20: Communication Protocol with User Authentication.
In the energy harvester module, all the software running on the sensor node
is contained. It has a submodule dedicated to data acquisition from the sensors.
Also, has a control I/O module in charge of sending control signals to the actu
ators, this part will be covered in detail on the next subsection. The harvester
communicates all the sensor data using a specialized module, that could also en
crypt and decrypt messages. The authentication server has modules to deal with
user login and data logging. One important feature of the server is its ability to
discover new harvesters that just connected to the system and incorporate them
as part of the sensing infrastructure. Also, the server has a multi-client support
communication module with encryption and decryption capabilities. The mobile
client will consist of the communication module plus a series of graphical interfaces
for displaying and controlling the sensor nodes.
36
Encryption/Decryption.
ModuleMonitoring
GUI Control
CommunicationModuleMobile. Observer MainGUI Harverster '
Locator
Mobile Monitoring Application
UserAuthentication
Data Logging
Transaction Loggin ;
Server.'MaimModule.:H a rv e rs te r
.- Discovery . <Services ■
Authentication Server
DAQ
Instrumentation ■. Main-Module
Communication ■ Module
Control 1/0
Encryption/'= Decryption,,
Module Energy Harvester
Figure 21: Software Architecture.
Figure 22 shows the storyboard of the user interaction with the mobile appli
cation through its interfaces. First, the user will be presented with the login GUI.
After that, the GUI harvester locator enables the user to choose any harvester
registered on the access server. From there, the user could select the monitoring
GUI to observe the measurements of the sensors, or the control GUI to adjust the
variables of the system on the local nodes.
37
Control
NodeSelection
MainMenu
Monitor
Figure 22: Mobile Application Graphical User Interface.
2.2 Networked Control System s
Every harvester is deployed in a very particular environment, for example,
the first energy harvester installed from NMSU was situated on the Drop 8 of
Elephant Butte irrigation channel. Drop 8 has two inlet gates and two drop gates
that can be raised or lowered to control the flow of water passing through the
drop, see Figure 23. Those gates could be controlled automatically to adjust
the amount of water going into the generator, and thus controlling the generated
voltage. Such implementation requires that the DAQ module has the ability to
output signals in both analog and digital formats. Those output signals could be
38
used to manipulate servo motors to raise or drop the gates in the Drop 8. Also,
these output signals could be used to control the voltage by means of additional
circuity.
(d) Drop 8 3D Model.
Figure 23: Elephant Butte Irrigation Channel Drop 8.
Initially, allowing the sensor nodes to provide outputs does not seem like a
big modification to the original architecture; but by definition, a sensor node will
not be a sensor node anymore, but an actuator node. When every sensor node
in the sensor network has the power to generate outputs based on its inputs, and
39
on the inputs of all the other nodes, a set of different problems arise. In fact, the
architecture is no longer a sensor network but a Networked Control System.
For the architecture to control the system, additional dynamics based on the
input/output relation must be performed at the node level. This dynamics, usu
ally in state-space representation, is not only affected by local variables, but also
from events happening in remote nodes. Figure 24 shows the mapping performed
on every control node a.
A ctuator,^ A ctuato r,^ A ctuator„iU
Control Node a
CommunicationModule
O ther Control Nodes
Figure 24: Control Nodes with Communication.
All the sensors of the node a, here named Sensor0>1. . . Sensorsa>n are mapped
directly into state variables I \ . . . /" and all actuators on the node, Actuatorail
. . . Actuator a_m, are mapped into state variables 0 * . . . 0™. One big challenge is
to map external state variables, for this purpose every control node has a secure
40
communication module that allows the transmission of data from other control
nodes, this data then could be mapped into inputs state variables 1% or output
state variables Oyx.
This mapping make it possible to express the local controller dynamic as in
the example of Figure 25. The expression describes the dynamics of a control
node n with has two inputs and two outputs. Variables Oln and 0 \ represent the
output state variables one and two of the node n. Each of this variables will be
associated with an actuator. The input variables and I'2 are associated with
sensors in our system. Note that the expression can have feedback, transforming
the outputs into inputs of our system.
Figure 25: Mathematical Model Incorporating Real Inputs and Outputs.
In the case that the control dynamics uses a variable that is sensed in an ex
ternal node data must be received from another node. On the other hand, when
41
the output of the expression is an external node, actualization data is sent to
the required node. Lines in Figure 25 also represents a communication channel
between one or more external nodes. Every one of this channels has a set of prob
abilistic distributions associated with it. Each distribution models randomness
in transmission time, packet loss, noise, etc. These models will be used by the
system to predict its running conditions.
The use of external variables inside the node is the main characteristic of
networked control systems. Some control applications only have a local effect, i.e.,
the only variables to control are the ones that reside on the same DAQ device.
This means that no communication or coordination is necessary. Other control
systems require a global effect on all the variables involved in the controller, for
example, the combined frequency of all generators must have minimum harmonics,
or the system must generate a combined constant power. This kind of operations
is called distributed control, but its primary execution relies on local controllers.
2.3 Intelligent Networked Control System s
To achieve the local desired behavior, standard control theory could be ap
plied. Other approach is intelligent control. Intelligent control uses techniques
discovered in artificial intelligence to modify the state of a system using encoded
knowledge or experience. The idea is that the control node has the ability to deal1
with conventional control as in Figure 25 and additionally it is able to implement
more sophisticated control such as fuzzy or neural controllers.
The neural controller is based on the artificial neuron model of Figure 26.
This model is an abstraction of the biological neuron, in which it is believed that
the input impulses of the neuron, in this case, must pass through
a resistance denoted by the weights wi,W2 ,W3 . The activation function / will
be trigger only if the sum of the input impulses is bigger than the bias 9. This
relation is shown in Equation 6 which assumes a direct output connected to an
actuator x of node a.
Bias6
n oActivatefunction Output
Ul2
H oWeights
Figure 26: Simple Mathematical Model of a Neuron.
n
°'i = f •“' < - * ) w
An artificial neural network is a collection of neurons interconnected in layers
like in Figure 27, The input layer will receive the signals to be processed. The
output layer will provide the desired response of the system. To achieve such
43
behavior, the neural network must be trained using one of many algorithms such
as backpropagation [193, 65, 187].
Input Hidden Outputlayer layer layer
Figure 27: Simple Local Intelligent Control on Node a Based on Artificial Neural
Networks.
For its use in control, the neural network must take its role of a universal
function approximation to relate the local sensor inputs to its outputs. In Figure
27, for example, the local input variables I \ . . . 7® are used to generate the output
signals to the actuators 01,01, and 0^. The inputs and outputs of the neural
network could also include variables that are remote, using the mapping performed
44
by the communication module of the control node.
A different approach in intelligent control is to use Fuzzy Logic. It is based on
the principle that linguistic variables could be modeled using fuzzy set. Such lin
guistic variables then could be introduced in fuzzy rules to perform inference and
relate inputs with outputs. For example in Figure 28 two linguistic variables are
described using sets. The first one represents “Frequency” and has three possible
values: low, acceptable and high. The fuzzification logic could be interpreted as
one Hz is a 100% low frequency and, 100 Hz is 0 % low frequency. This model
ing is shown On Figure 28 even though no scale was specified for the frequency.
Another possible linguistic variable is “Water Flow” with similar modeling but
different units and scale.
low acceptable high low normal high
0Frequency Hz Water Flow rrv
s
Figure 28: Fuzzy Sets Representing a Particular Rule.
Using these linguistic variables it is possible to specify control rules for the
generator, for example:
45
Rule 1:
IF frequency is low
THEN water flow is high
Rule 1 means that if the generator is producing a voltage at low frequency, the
system must increase the water flow to high to achieve the desired state. A
full fuzzy inference system must have many rules to relate the input variables to
the output variables of our system. In Figure 29 an example fuzzy controller is
presented that relates local inputs / * . . . I" and remote inputs and outputs, in this
case, 01 to the local outputs O*. . . 0 ™ and remote outputs 0®
Control Node a
FuzzyInference
Engine
Figure 29: Control Node Implementing a Fuzzy Controller.
46
Each node could also combine all technologies producing hybrid systems such
as neuro-fuzzy controllers.
2.4 M athem atical M odeling of Networked Control System s
The model of our networked control system must consider a peculiar charac
teristic of the architecture: the communication section work with bursts of data,
i.e., data is not always available. On the other hand, the generator is constantly
working. In Figure 30 a general separation of the discrete/continuous components
is shown.
External
ControlNode
Discrete
/ Cont.
ControlServer
RegulationLayer
(Control)
CoordinationLayer
(Communication)
PhysicalLayer
(Generator)
Figure 30: Discrete and Continuous Elements of a Control Node.
47
This behavior could be modeled using an automaton like in Figure 31. Every
transition of the automaton represents a discrete state of the system. Each discrete
state is governed by a different continuous dynamic. The example of Figure 31,
shows two possible states of the system O N and OFF. In a networked control
system, the state of the whole application depends on the discrete states of the
transmission. The continuous state is O* € M, and the discrete states are q e
{ON, OFF}. The change of 0* is described by the differential equation a — —aO}i
when in sta te1 q = OFF. The state q can not jump from O F F to O N unless
0} > 3. A simulation of this simple hybrid system can be seen in Figure 32.
O la < 91
O N, O F F
Figure 31: Hybrid System.
Formally a hybrid control system could be expressed using a Hybrid Automa
ton which describes the evolution in time of the values of a discrete and continuous
state variables:
D efin ition 2.1. (H ybrid A u to m ato n ) A hybrid automaton H is an octuple
H = (Q , X, f , Init , Dom, E, G, R ), where:
• Q = {?i> 92, • ■ •} is a finite set of discrete states;
48
• X = Rn is a set of continuous states;
• /( ' ) ■) ■ Q is a vector field;
• I nit C Q x X is a set of initial states;
• Dom(-) : Q —> 2X is a domain;
• E C Q x Q is a set of edges;
• G(-) : E -> 2X is a guard condition;
• R(-} •) : E x X -> 2X is a reset map.
Recall that 2X is the power set of X. On Definition 2.1, the function Dom
maps a set of continuous states Dom(q) C Rn to each discrete state q £ Q. The
100
O
0 0.2 0.4 0.6 0.8 1 1.2 1.4t
Figure 32: Simulation of the Hybrid system of Figure 31.
49
relation (q,x) € Q x X is referred a the state of H.
The hybrid automata define the possible evolution of the state. In a nutshell,
starting from an initial value (q0,x o) G I nit, the continuous state x changes
according to the differential equation x — f(qo,x) with x(0) — x$, while the
discrete state q remains constant, i.e. , q(t) — qo■ Continuous change remains as
long as x remains in Dom(q0). If at a point the continuous state x reaches the
guard G(qo, q±) C ]Rn of some edge (qQj q\) € E, the discrete state may change to q\.
At the same time, the continuous state is reset to some value in R(q0i q\,x) C Rn.
After this discrete transition, continuous changes resumes, and the whole process
is repeated.
Hybrid automata involve both continuous changes determined by differential
equations and discrete transitions determined by a directed graph (like an au
tomaton). The analysis of Hybrid automata implicates the computation of its
trajectories, i.e., to check if the system can reach a state given a set of inputs.
One big drawback of hybrid automata is the ability to produce models that are
unreasonable, either physically or mathematically.
One remark to make about the networked control systems is that the imple
mentation is completely different from the theory, as a simple example, a state
variable could be used as an input or output, while the implementation requires
a distinction between inputs(sensors) and outputs(actuators).
50
2.5 D istributed Control in Networked Control System s
The ability to achieve a global state using all the variables of the networked
control system employing autonomous controllers is called Distributed control.
Distributed Control Systems often have a central operator which also supervises
the control status of all combined nodes. This is because most implementations
of distributed control over networked control systems are in the manufacturing
industry, this scheme is called hierarchical control. In hierarchical control, there
is a bottom layer consisting of sensors and actuators. Then another layer with
the micro-controllers required to acquire and process the inputs. Then a super
visory layer with computers that will condense all the information and present a
console to the user. This hierarchy could have more supervisory layers to achieve
different control projects. One drawback with the hierarchical control is that the
supervisory layers must receive the perceptions of the inferior layers and then
communicate a proper control action back to all inferior nodes.
In the case that the control must act fastly over the plant, there will not be time
to transmit the inputs and wait for the response. The control node must be able
to take some autonomous control decisions. Those decisions must be consistent
with the general objective of the distributed control, i.e., every autonomous part
must contribute to the general success of the control. One example of autonomous
control is the “Subsumption architecture” [23], this method was a prevalent way
51
to implement behavior-based robotics in the eighties.
A very especial case of distributed control is when the optimal state must be
achieved by the combined dynamics of the nodes, i.e., optimal distributed control.
This task is way much harder that non-optimal control since the reachable state
of the system must be a maximum or minimum. Other example of optimization
is to minimize the response time of the system. In general, the system must
complete the optimization task by taking in consideration restrictions inherent to
the problem to solve.
Some methods exist to find an optimal solution, in particular, the greedy
algorithms have been used extensively in optimization, a pair of examples could
be found in [119] and [83]. In a nutshell, a greedy algorithm always makes the
“greedy choice” , i.e., it always selects the local minimum hoping to get the global
one. E.g., the knapsack problem states that if we have a set of elements with
value and weight, which elements should choose to maximize the value of items
that we can carry in a limited weight knapsack. The greedy choice is to pick
always the item that weights the less and has more value. If the greedy choice
is always applied, it is guaranteed that the knapsack will be optimized for value
and weight. The greedy approach works great in discrete cases, and the optimal
value is sure to be found if the problem to optimize is reduced to a Matroid [36]
or more general to a Greedoid [77]. The idea of the project is for each node to be
able to implement a greedy choice, i.e., each node will optimize locally. In some
problems, this behavior will achieve the global optimum. Some other examples of
greedy algorithms in control could be consulted in [82] and [118].
Unfortunately, most of the control situations are not discrete in nature, like the
voltage. For these problems, Intelligent Optimization could be a viable alternative.
Particularly one bio-inspired algorithm called Particle Swarm Optimization (PSO)
can be used to solve more general optimization problems. The particle Swarm
optimization works emulating a bird flock, i.e., a population of candidate solutions
is given; these solutions then move into the search space according to a specific
set of social rules. W ith no specific way to measure the quality of a solution, the
optimum is obtained by the particles moving towards the solution. Some examples
of particle Swarm optimization applied to control could be found in [53] and [91].
53
3 D EV ELO PM EN T
This chapter presents the analysis, design, and implementation of the system
proposed on previous sections. In a nutshell, the project represents a Computer-
Aided Control Engineering (CACE) tool. One early example of such tools is pre
sented in [68]. Additionally, the project involves the implementation of a frame
work [164], One main deviation is that the CACE tool is designed to handle
distributed control, and the framework is able to deal with security implementing
also wireless communication and mobile devices. Also, the tool allows the cre
ation of intelligent control scheme based on artificial neural networks and fuzzy
logic. On top of all, the infrastructure implemented will provide an environment
to implement optimization schemes like PSO and greedy over interconnected het
erogeneous components.
The system is divided into two sections: operation and analytics. The opera
tion will deal with the functioning of the communication while the analytics part
will deal with the model extraction and stability test. Both parts are required to
work in harmony to achieve optimal distributed control. The programs will not
provide this control, but rather it is a tool that will provide an environment to
design, test, implement and operate automatic control models which goal is to
achieve a set of optimal conditions. All this functionally will be accomplished by
configuring an interface that will show different levels of granularity of the real
implementation of the networked control system.
For this design, it is assumed that the electronics part will be readily avail
able, i.e., sensors and signal conditioning. With this consideration the rest of the
components are software. Thus a software engineering methodology was used to
construct the tool.
The first step is to simplify the architecture of Figure 19 eliminating all the
electronics in the node. Figure 33 shows the main components of the system.
A set of wireless nodes connected to the server through the Internet. A set of
mobile clients or observers is connected to the server. The server has a central
monitoring system that allows a Control Engineer to operate and adjust globalI
system properties.
Server
(«.»)
Internet Internet
(«.»)
Mobile Observers Central Observer Control Nodes
Figure 33: General Architecture.
55
3.1 System Design
The Unified Modeling language (UML) was used to define the layout of the
software. UML is widely used in computer science and even in control engineering,
see [134]. Also, has capabilities for describing distributed and real-time applica
tions [50]. UML is furthermore suitable to design automatic control systems like
in [158]. An excellent source is [17] in which UML is explained by its creators.
For this project many UML models were produced, here a subset of the most
critical components and scenarios will be presented. The design aims to translate
the requirements established during the previous phases into a representation that
could be easily converted into a computer program.
The development order of UML models for this project is Use Case, Sequence
and Class diagrams. With this subset of models, many of this software featuresI
could be explained. Several characteristics of the diagrams were omitted such as
temporal variables, private internal methods, or nonrelevant messages in order to
maintain clear readable figures.
3.1.1 U se Case Diagrams
The Use Case Diagram shows the way the system is used by internal and
external entities. External entities are called Actors and are represented by stick
persons inside use case diagrams. Every oval represents a way in which the sys-
56
tem is used. Use Case diagrams were established for each mayor architectonic
component.
Figure 34 shows the first Use Case of the system. This use case describe
the uses of the node control. For the node, the server, actuators, and sensors
all represent external components thus they are modeled as Actors. A verbal
description of the diagram is that the server uses a communication module to
send and receive data to the actuators and from the sensors.
A c t u a t o r
S e n s o r
Figure 34: UML Use Case Diagram for Control Node.
As well, the server uses the communication module to send update orders to
the autonomous control module. The Autonomous control logic uses the math
57
C o n t r o l N o d e
.Q u ltnta M a t h M o d u l e
[C o m m u n i c a t io n l
*S e r v e r
module, which is described later, to perform the mathematical operation required
to adjust the system’s state. Sensors must use a DAQ module which has all scaling
and transformations needed for each of the appropriate sensors. The Actuator uses
the system by an output module which will have the necessary interaction logic
for each particular actuator.
The node itself becomes an external actor every time it uses the mathematical
library module. Figure 35 shows this scenario. This mathematical module can
solve symbolic math and linear algebra. The libraries must include functions to
solve systems of differential equations numerically.
M a t h M o d u l e
P .S O
E x t e r n a l C a l l e r
G r e e d y <
S y m b o l i c m a t h
Figure 35: UML Use Case Diagram for the Math Module.
These libraries must support the creation of more complex functions and even
58
larger libraries, such as neural network, fuzzy logic, particle swarm optimization,
greedy optimization, deep learning, etc.
The server’s Use Case is shown in Figure 36. Here the mobile clients and the
control node are external actors. The mobile observers use the communication
module to send data request or control actions to the mobile client. The network
statistics module will keep information about the network traffic. This information
will be used by the control module to be considered in the actual control scheme.
The control module uses the communication module to send and receive state
updates to the control nodes. The control module uses the math library and the
optimization module to compute the new states. The data logging module will
store the transmitted information from the communication module. The location
services module will be in charge of keeping track of a list of control nodes andi
their geographical position. Finally, the Control Engineer (CE) has to use the
server through a GUI. This GUI uses the user authentication module to verify the
user credentials. Then the CE could be able to access all internal modules of the
system.
Lastly, the Mobile Client’s Use Case diagram is shown in Figure 37. Prom the
mobile client perspective, the server and the CE both are external actors. The
CE uses the mobile client to monitor and control state variables using respective
GUIs. These monitor and control GUIs use the communication module to contact
the server for an update or to read the general system defined variables.
59
. O p t i m i z a ! io n j - * M o d u l e .
C o n t r o l N o d e
d M a t h M o d u l e
Figure 36: UML Use Case Diagram for the Server.
Diagrams in Figures 34, 35, 36 and 37 could be all put together into a big
System’s Use Case. The primary purpose of the Use Cases is to define the main
objects to be implemented. Three main classes are observed: the client, the node,
and the server. Each containing several subclasses to achieve their tasks.
3.1.2 Sequence Diagrams
The Use Case Diagram shows the way the system is used but it lacks the
concept of time, i.e., the Use Case does not show the sequence of messages in the
time required to achieve one task. The Sequence Diagram is introduced to solve
this problem. In a Sequence Diagram, a set of classes are displayed on top, and
M o b i l e C l i e n t
M i W d U I l
Figure 37: UML Use Case Diagram for the Mobile Client.
each class has a line of life. Between two classes a synchronous or an asynchronous
message could be sent. The time is represented downwards in the object’s line
of life. There, must be a sequence of messages for every mayor scenario of the
software.
Figure 38 shows the scenario when a CE reads values from the control node.
The objects b and c belongs to the mobile client. Objects d, e, / , g, and h are part
of the server. The control node has objects i and j .
The first part of this diagram shows the login process. The CE sends a message
to the monitor GUI with his username and password. Then the communication
module of the mobile client sends this Authentication message to the server com
munication module. Then the user authentication module verifies the presented
credentials. If the user is verified, a message is sent to the location services.
61
62
Figu
re
38:
UML
Sequ
ence
Di
agra
m
for
Read
ing
Dat
a fro
m a
Mob
ile
Obs
erve
r.
This module loops through all nodes and using the communication module
sends an alive message to every node. If the control node gets the alive message
from the server, it sends an acknowledgment to the server using its communication
module. The gathered list of alive nodes is then sent to the mobile client for the
CE to select a node to observe. Once the node was selected, a data message is
sent to the control node from the server using the communication modules of both
components. The data is then sent to the CE and also to the data logging module.
Statistics about the communication is stored by the network stats module to be
considered in the automatic control.
In Figure 39 the sequence of autonomous control is shown. Objects a, b, c, d
and e belong to the server while objects / , g , h, i, j are part of the control node. In
order to update the control state, the autonomous control module requests inputs
to the data acquisition module. Then it sends the variables to the server using the
communication module. The autonomous control module computes a new state
using the math lib module. The new state is then transmitted to the actuators
using the output module. This update is the sent using the communication module
to the server. The server gets this update using its communication module. The
server will update its log and it also updates the value of the reading to the control
module. A local manual update will have an update message sent from the node
to the server. The server will update ts logo and control states.
63
64
Figu
re
39:
UML
Sequ
ence
Di
agra
m
for
Aut
onom
ous
Con
trol
.
In the case the server has a global optimization scheme, the server will compute
the new state. A global update message will send the update values from the server
to the control node. This sequence will loop indefinitely until a manual control
action overrides the automatic scheme.
3.1.3 Class Diagrams
The last part of the design is to generate a hierarchy of classes that implement
all the messages established on the sequence diagrams. Related classes are then
put together in packages. The project has three main packages: the control node
package, the server package, and the client package.
In Figure 40 a simplified class diagram for the control node package is shown.
This package consists of five principal classes. The control Node is the main class.
It has methods to respond to global adjusts from the central server, as well as
methods to respond to the autonomous control. Its many attributes must store the
control parameters to drive the node itself. The control node has a communication
class. This class is in charge of dealing with the sending and receiving of messages
to the server. The output class encapsulates all the methods required to send
values to the actuators. The input class will have all methods necessary to read
values from the sensors. The control Node class has a dependency on the math lib
packages since the autonomous control is required to perform some mathematical
operation to update the node state variables.
C ontro l N ode Package
M a th L ib
M a th
I n p u t s : v e c t o r S t a t e O u t p u t s : v e c t o r S t a t e
so lv e (p ro b le m ) : r e su lt
C o n tr o l N o d e
C o n t r o l P a r a m e t e r s : u i n t
a u t o n o m o u s C o n t r o l ( a c t u a l S t a t e ) : S t a t e g l o b a l C o n t r o l A d j u s t ( n e w S t a t e )
O u tp u t
n u m b e r O u t p u t s : u i n t
w r i t e O u t p u t s : S t a t e
SiMSS: ISSisS'XC o m m u n ic a t io nI n p u t s
p o r t N u m b e r : u i n tn u m b e r l n p u t s
s e n d L o c a l S t a t e ( ) g e t G l o b a l S t a t e ( )
r e a d i n p u t s : S t a t e
Figure 40: UML Class Diagram for the Control Node.
Figure 41 shows the Mathematical Library Package. Its main class, Math,
has methods to solve systems equations and to perform advanced mathematical
formulas on a set of stored variables. This main class uses a set of external
classes to solve different problems. The fact that the library is divided, not only
obey the reason of encapsulation but also has to be with the different computing
capabilities of the node, i.e., no all the nodes support all the external classes. One
of the external libraries adds a class that implements artificial neural networks.
It has methods to train different architectures, and it can support many numbers
of layers. The math lib package also contains a fuzzy logic class with methods
able to fuzzify and to defuzzify system variables. The fuzzy class is capable of
inference over a stored set of rules.
M a th L ib
A N N
n u m L a y e r : u i n t n u m l n p u t s : u i n t n u m O u t p u t s : u i n t O D E S o l v e r ( )
F i n i t e E l e n i e n t A n a l y s i s ( )F i n i t e D i f e r e n c e s ( )L i n e a r A l g e b r a ( )
M a thF u z z y
m a t h V a r i a b l e s
c o m p u t e Qs o l v e ( )
G r e e d y
P S O
O p t i m i z e Qg r e e d y C h o i c e ( )
p o p u l a t i o n : u i n t S y m b o l ic M a th
O b j e t i v e F u n c t i o n ( )O p t i m i z e
S y n i b o h c V a n a b l e s
S o I v e Q
Figure 41: UML Class Diagram for the Math Package.
Other important features of the Math Lib package are the Particle Swarm
Optimization module and the discrete greedy class. Those two classes provide
methods to elaborate distributed optimal control.
The core of the Math Lib package is the numerical solvers and symbolic math
classes which both provide a robust set of functions to manipulate and solve
mathematical systems to control.
Figure 42 displays the Server package. The main class is the Server class that
has methods to listen for connections and accepts them once requested. Also, this
class has a queue to process the responses and queries to the server.
The server is in contact with the communication class, which is responsible
for implementing the protocol, i.e., sending and receiving data from the multiple
clients and nodes. This communication class is composed of a network statistic
class. The focus of this class is to predict the delay of the communication between
nodes. The communication class is also composed of a location services class which
will keep a list of all active nodes and observers in the network. The server has an
aggregate class to log all information pertaining the system, i.e., variable values,
control actions, etc. Another aggregate class of the server is the control class. This
class is resporisible for the distributed automatic control of the system. Also, it
is responsible for performing stability tests on the system’s model. The control
class uses the math library package in Figure 41 and an additional optimization
class with all methods required to implement optimal control. The server is used
by a CE through a graphical user interface, which it has its class. The server also
Server
G U I C o n tr o lM a th
a c t u a l U s e rs y s t e m M o d e l
v a r i a b l e sA n a l y t i c sC o n t r o lD e s ig nT e s t
S o lv e Q
O p t im iz a t io n
r e e s t r i c t i o n s
D a t a L o g g in g S e r v e rl o c a l O p t i m i z e ( )g l o b a l O p t i m i z e ( )D a t a a c t i v i t y Q u e u e
r e s p o n d ( )l i s t e n ( )a c c e p t ( ) U s e r A u th
u s e r L i s t
L o c a t io n S e r v ic e s
N e tw o r k S t a t sa c t i v e N o d e s : u i n t N o d e L o c a t i o n s
C o m m u n ic a t io n s
n o d e S t a t s e x p e c t e d D e la y s
c h e n t L i s tA l i v e ? ( n o d e )f i n d N o d e ( )
s e n d ( )r e c e i v e ( )
s t a t A n a l y s i s ( )d e l a y P r e d i c t i o n
Figure 42: UML Class Diagram for the Server Package.
has a class dedicated to authenticate users and assign their respective roles.
The last system package is the mobile client of Figure 43. These classes are
intended to run on mobile devices and therefore must be light. The client class is
in charge of executing orders received from the User GUI or the Server. To get
the updates from the servers an aggregate class of communication is introduced.
The GUI is divided into two classes: one for control the variables and other to
69
read the sensors.
C lie n t ' :
Or e q u e s t Qu p d a t e Qc o n n e c t Q
a c t i v i t y Q u e u e
C lie n t
s e r v e r A d d r e s s
s e n d ( )r e c e i v e ( )
C o m m u n ic a t io n
r e a d V a r i a b l e Qc h a n g e N o d e ( )p l o t V a r i a b l e ( )
M o n ito r G U I
U s e r
v a r i a b l e sn o d e
m o d i f y V a r i a b l e ( )r e s t o r e V a r i a b l e ( )
C o n tr o l G U I
Figure 43: UML Class Diagram for the Client Package.
3.2 Im plem entation
This section will present some implementation details about the system. Di
agrams of Subsection 3.1.3 give a brief insight of the system’s interior structure.
Those diagrams directly translate to code. The main language chosen to imple
ment this project was Java. Java was selected not only because it is the number
one programming language as June 2017 (according to the TIOBE index [160]);
Java was chosen for the great number of available open source libraries and its
70
portability. Also, Java is the main language on the Android platform, which was
chosen for this project. Many examples exist of Java used in control such as
[41, 16, 137]. The control node program was developed using a language accord
ing to its computing capabilities. Three main applications were developed: the
server, the mobile application, and the control node.
3.2.1 Server
This section describes the Server of the Open CACE system. The server is the
system’s most important part. To explain its implementation a brief description
of its GUIs is presented in this subsection.
Figure 44 shows the login interface of the server. This user authentication GUI
is presented to the CE through the central observer console, see Figure 33, and it
runs directly on the server platform. The open CACE server could be executed re
motely u§ing a terminal
Once the CE provides his username
and password, the user credentials
will be authenticated against the user
database. Different users have distinct
access levels: Administrative, control
engineer, and report generator. The
administrative user will be in charge
71
connected to the server.
12
Open CACES e r v e r E dition
U se r A u th e n tic a tio n : ..................................................
U sernam e: jnah itt
Passw ord:
Login
Figure 44: Server Login GUI.
of creating users and assigning systems
roles. The report generator can access the data logs of the server, and its main
purpose is to analyze in real time the system’s behavior. Lastly, the control user
has access to modify the distributed control scheme.
Once the CE is authenticated the system preview GUI of Figure 45 is shown to
him. The system preview GUI is divided into three main sections. The Preview
map, the geographic information section and the system status section.
' C on tro l R unning :
j {N euro-Fuzzy
U se r A u th e
' Mobile C lien ts:
! R e ce iv e d P a c k e ts :
. S e n t P a c k e ts :
; 1 ,7 9 2 ,4 6 3
: A ctive N o d es :
1 T ota l S e n so rs :
T ota l A c tu a to rs :
P re v iew Z one
EXITL ong: 0 [ l a s C ru c e s , NM
Figure 45: Server System Preview Login GUI.
A screen detail of the preview map is shown in Figure 46. This map is divided
into three layers. The first layer shows the data nodes. In this section, a selected
72
I
subset of the system control nodes is displayed in real time with an excerpt of
their sensor readings. The second layer shows the network links between each
control node. Every link displays information related to the connection such
as the number of lost packets, of average delay. Each node of the data section is
represented on the network layer. The preview map represents this by connections
(red lines) going from the first layer to the second. The last layer represents the
control logic that the whole system is running. It shows a directed graph relating
variables. These variables could be mapped to inputs or outputs of the data layer.
This mapping is represented on the map preview by connection (blue lines) from
the second layer to the control layer. It is important to remember that the preview
is an accurate’ depiction of the real system but hides many details. This layered
map preview is interactive, i.e., responds to the user’s mouse updating its content.
Through this interaction the CE is able to examine all system’s details.
Another component of the system preview GUI is the System Status section.
This part will provide a quick report on the main system characteristic. Each
layer properties are displayed at their level on the preview map. In the control
layer level, the status shows the actual control scheme running and the preferred
optimization method. At the network layer level, the status displays the number
of connections and several statistics about the transmission of packets. The data
layer level displays, on the status section, information about the active nodes and
the total number of sensors and actuators on the system.
74
Figu
re
46:
Scre
en
Det
ail
of the
Sy
stem
Prev
iew
GU
I
The last part of the system preview GUI is the geographical control. This
section allows the CE to select the preview section of the system. It also allows
the adjustment of the displayed features on the map and the zoom level.
The preview map interactivity is achieved when the CE clicks inside of any
of the layers. Each layer is defined by a rectangle as shown in Figure 46. Three
scenarios could happen here: i) If the CE clicks on the data layer then, the GUI
of Figure 47 is shown, ii) If the CE clicks on the network layer then the GUI of
Figure 49 is open, iii) Finally, if the CE clicks on the control layer, the GUI ofi
Figure 51 will be activated.
This layered preview map is generated using actual information from the sys
tem. It consists of a bitmap that can be displayed on the server application or
transmitted to another program such as a web page or mobile application. The
open CACE server considers the most relevant information to discriminate dis
played data and improve automatically readability. Also, it could be configured
to ensure the display of certain components that could be used during design or
synthesis of the system. The system preview map represents graphically all the
components and interactions of the system.
When the CE clicks click on the data layer, the GUI of Figure 47 is shown as it
was mentioned before. This GUI is composed of two main sections: the map and
the status section. The map shows a geographic rectangle using the Google Maps
Application Program Interface (API), see Appendix C for a complete description
75
of the libraries used in this project. Over this map, the set of control nodes is
displayed using the JUNG library.
Q y 5yst«m Prewi
File Edit
Geographic infbn Geo C ook
P Q D ata Layer N o d e S e lec tion O’J!
H ap: .............................. *.......................
\
<§2$
Y ' %
. G eographic Coordinates:
: Upper Left Com er:
: Longitude:
: [-107(209SI _]i Lower Rigtht Com er:
; la ttitu d e :
; h u ™ 3.5 1
; Longitude:
;
-Node I n f o : ..................
Node Position:
Longitude:
[-106.7637 ]
Node Name:
[B ID Drop 8
, j . N ode D etafe \
» Refresh map
Figure 47: Server Data Layer Node Selection GUI.
The status section displays the geographic coordinates that enclose the selected
control nodes. Each control node in the map could be clicked to select a particular
node to inspect. When a node is selected its information is displayed on the status
section. If the CE double-clicks the node, then the Data GUI of Figure 48 is
displayed.
The data GUI is the main interface to see the instrumentation of each node.
This GUI can add visualization panes to display different type of node variables.
76
fi>l System Prtv:
f-ite Edit
( j j j 0» i2 tsy-fcf Mc«l<» M:«<t:pri GUI
Map: ...............................
'S , , .
Geographic Coordnates:
Upper Left Comer:
Lattitude:
[& j In strum en ta tion GUI for N o d e : 1AC034
File Edit M easu rem en t Panel
3056 :.....
ktude:
T e m p e r a tu r e : ...................G enerator Temperature:
W ater T enperature:
Choose Graph:
[Genera torT errp
G e n T e m p . \ Inlet P resage :
t -e T " 11 ; , ( ib ] p s i
| VIH) j j O utlet Presure:
| I | 25
( Computed Discharge:
i M, i i ; ; [3 l m A3/s
l ^1 r! i • O w ose Graph:
1 i ■ ! [W ater Speed v j
W ater S p eed
Figure 48: Data of Selected Node GUI.
: St-2095793 !
f f R t f i t Corner:
I 387029
Power Variables:Current: !
Acceleration:
O iocse Sensor:voltage Acceleration! Amp.
{Generator v ;
Code:
Frequency:YaMS
Choose Graph Zaxis:| n -X jrir -+-T axil -ftrZ airii |
wg<mfresh map
These graphs are implemented using the JFreeChart library. In the example of
Figure 48, the GUI is configured to show generator temperature, water speed,
power generated and three-axial acceleration.
On the other hand, if the CE clicks on the network layer of the GUI of Figure
45 the Link selection GUI of Figure 49 is open, as it was also mentioned before.
The main emphasis of this GUI is to select the link edges between nodes. It
contains two main components: the map and the status panel. In the map section,
the selected set of nodes is shown. Additionally, link lines are displayed, each link
edge represents an active connection between nodes. These links are responsive
77
jj£) lysttm Previe
File Edit
j f j Link Selection GUI
•Map: .....................
Geographic inform Geo Coord?
*
<
*
"lip
□ X
'N etw ork Status:
N urbe r o f I r t s :
I16 iA ctrvelhks:
Link In fo : ..................• Up Speed:
: [&4_! tops.■ Down Speed:
Mbps.
Figure 49: Link Selection GUI.
to the CE interaction. If one click is performed upon a link, its status is shown
in the status panel. With two clicks on the link, the Network Analysis GUI of
Figure 50 is presented.
The Network Analysis GUI is divided into two sections: global and local statis
tics. The global section displays network information gathered during the system
operation such as average up/download speeds. Also with this information can
perform global network related operations like modeling with probabilistic dis-i
tributions the network behavior. Those distributions are then used to predictI
network contingencies and generate a more robust operation. The local section
78
(2} lin k Selection GUI
d System Prfvie
File Edit
Ai
Geogr
foj N etw ork Analysis
r Global S tatis tic s :..............-.. ............. ..................
; A verage Speed {UP) [ l o t T l M b p s
; A verage Speed {DOWN) jlQO ] hfops
• Percentage o f Orop Packages [lOO ]
Network Degree o f Dstrtoution:
p .Compute-.-: j
Markov Random Reid (MRF) model:
[~ .C onV ite t~ |
KemeHearrBng regression Models:
Packet Lost Dist.0.020 1
0.015
0.010 i
0-005
0.000 JNum. ofPackets
Displaying graph oft [Packet l o s t Pistrhution
A v era g e C on n ection S p e e d
S tatx Markov Model
Compute
Between Node: J1ACD34
and Node: }2BAAFE
Oroped Packages: [lOO
Displaying Graph of: p i c k e t lost:.-;.. .; — y ]
Figure 50: Network Analysis GUI.
Network Status: - -
Number o f L H s:
; & _____________ I
• Active links:
: P 1. Link In fo :-- Up Speed:
: L«______ ] Mbps.
: Dawn Speed:
; § _________i Mbps.
displays information related to the network operation of the selected link between
nodes. The system uses this information to solve statistical models to forecast
future connectivity behavior.
The last option the CE has on the GUI of Figure 45 is to click on the con
trol layer to activate the Control Design GUI of Figure 51. This control GUI is
composed of four sections: The control graph, the node specification, the control
scheme section and the system operation.
The most important section of the control GUI is the control graph. It rep-
79
f#>| S y s te m P rev iew Wmc!o*v
Flie Edit
z
z
zj
G eograpbc
Geo|
lo n g : )
(751- m -m J7i jn .. „ n>n, Control Running:
a a L c s i t iJ L
C o n tro l D esign GUI
Control G raph • - -- N ode Specification:
■ S e lec ted N ode:
o!,
5 6 7 8 9
| . f - T y p e : - ■............
i (5) O u tp u t variable
i 1 ; O In teg ra to r
; ;' O0erivator; O Summation
O S u b t ra c t io n
| ? i O C o n s tan t
I O C u s to m
|I ! Control Schem as: - ....
| NwdO
, System O peration:
S tabS ty Analysis B 1 {~ Run : j O p tir r a
Figure 51: Control Design GUI.
resents graphically the relations established between the inputs and the outputs
of the system. This graph is the model of the automatic control scheme running
on the overall system. This control graph is fully editable. Details about the
selected node could be obtained by clicking once on each node; this information
is displayed on the node specification section. With a double-click on the node,
the GUI of Figure 53 is displayed. That GUI allows the custom specification of
each node mapping. This custom code could be Octave or Java based.
The control scheme section allows the CE to add global neuro, fuzzy and
hybrid control. Those control scheme could be combined with the control graph
80
to generate intelligent control modes.
The system operation section is used to invocate the main operations on the
system: Stability analysis, simulation, execution, and optimization. In the case
the CE presses the stability analysis, the GUI of Figure 52 is shown.
OS
fl> l S y s tem P rev iew W in d o w
File Edit
A
A
A
G eographic
Geo
L o n g :!
j j>i C o n tro l D esign GUI
- Control G raph -.....
■O1
/'
, <
- S ystem O peration: ......
j StefcSty Analysis
§>j S tab ility A nalysis
•-Plot:-----------*--------
Nyquist Diagram
0 .4 0.0 Q.e 1.0 1.4 1.6 1 8 2.0 2 .2 2 .4 2 8 2 8
Analysts O ptions; -
G raph Type: m iyq iis t Contour] v j p Edit G raph O ptions
J
Figure 52: Stability Analysis GUI.
The Stability analysis GUI of Figure 52 has the preconfigured procedures to
compute simple or custom stability analysis on the system model. This GUI also
can display graphs such as the Nyquist Contour.
The CE could click on each graph’s node to enter the custom node specification
81
GUI. This interface shown in Figure 53 allows adding a mapping between the
system variables programmatically. Using the JavaOctave library, any Octave
program could be inserted as node activity. Also, the vast amount of scientific
and mathematics Java libraries allows generating an endless amount of complex
expressions.
f i l System Preview Window
Fite E dit
- X
[13 Custom Node. Specification: — L3 X
....Control
C ontrol Gr;
n :
zO ctav e a id e :
a X
f u n c t i o n x d o t *■ f ( x , t ) ifca b o n : --
ffl : r “ 0 . 2 5 ; N ode:
I l 1 ;
: U ser A uthent
-___
___
\__
____
_ i 0 b * 0 . 1 6 ;
c - 0 . 9 ;
d » 0 . 8 ;
x d o t ( 1 ) = r * x ( 1 J * ( 1 - x ( l ) / 3 c ) - a * x ( 1 ) * x ( 2 ) / ( 1 + x d o t ( 2 ) - c * a . * x ( l ) * x ( 2 ) / U + b * x ( l ) J - d * x ( 2 ) ;
e n d f u n c c i o n
it Variable
u t variable
g ra te r
irator
matron
;tr action
: !
s ta n t 1 :
| T e s t E xternally j j A ccept j om
z \ ZS •O e ta te J
C ontro l S d iem a s : •
G eographic
Geo
Long;
; <
- S ys tem O pera tion : - -
V
>
| N eural |
| Fuzzy ]
! C ustom I
: [ S tabS ty Analysis ' j Simulate j ^ R un { O ptim iration j
' - J
Figure 53: Custom Node Specification GUI.
82
3.2.2 M obile O bserver A pp lication
This section describes the mobile application used to interact with open CASE
system. This application was developed using the Java language, to capture the
Android market. An implementation in Swift Language is in progress to support
Apple devices.
The mobile client must support the main features of the main observer. Many
of the mobile application activities have a parallel GUI on the server. When the
application is started on a mobile device, the Login activity of Figure 54 is dis
played. After the user authentication process, the main menu activity of Figure
55 is activated. From this menu, the CE has three options: System preview; auto
matic control;'and node observation and control. If the CE clicks on the “System
Preview” button, then the activity of Figure 56a is displayed. If the “Automatic
Control” is clicked, then the activity of Figure 56b is activated. Finally, if the
“Node Observation and Control” button is clicked, then the activity of Figure 57
is shown.
The available actions from this main menu depend on the user authentication,
i.e., not all users have all buttons activated. The first two buttons correspond
to events that require Access to the Server services, see Figure 56 while the last
button is related to control node activities.
Figure 56a contacts the server to acquire the system preview map. This ac-
83
Open CADCE (c) 2 01 7 by.Nahitt Padilla
S y s te m . Login In fo rm atio n : E m ail. ' <
n a h it t@ h o tm a il.c o m
tivity is analogous the server GUI of Figure 45, although it is not yet interactive
as the server one. It has buttons to access the systems status dialog, which
presents information about the system operation and control scheme being used.
This activity also has a connection
overview button to display a dialog
with information about the underly
ing network connections. Figure 56bi
also contacts the server to get the sys
tem ’s automatic control graph. This
activity is analogous to the Server GUI
of Figure 51. 1 It displays the control1
graph but does not allow to modify it
yet. With its buttons allows the in
spection of the control node properties,
control scheme and also allows to verify
in which operation mode is the system
working.
Passw ord (oplio'iel)
SIGN IN OR REGISTER
1 2 3 4 5 6 7 8 9 0
q w e r t y u i o p
a s d f g h j k I
k z x c v b n m Q
i ' . S ig n in7123
Figure 54: Login Activity.
If the CE clicks from the activity of Figure 55 the button of “node observation
and control” then the activity of Figure 57 is invoked. This mobile client activity
is a simplified version of the GUI of Figure 47. It uses the Google Maps API to
display the geographic position of every control node in the system. It has two
84
main buttons: “View Node Variables
First, the CE has to click on the control
node over the map to select the node
to monitor or control. The information
of the selected node will be displayed
such as name and node code. The CE
has two options. If the first button is
clicked then the activity of Figure 47
is displayed, but if the second one is
clicked then the activity of Figure 59
is activated. This activity allows all
the Google Maps functionality such as
zooming and panning. Saved map sec
tions could be selected and displayed
by using a drop-down menu. Activities
launched from' this screen could be cataloged into instrumentation such a the Fig
ure 58 or control like in Figure 59. The first ones allow the CE to monitor node
variables while the second to modify their values.
After clicking on the “View Node Variables” button, the CE is presented with
the instrumentation activities of Figure 58. All plots in this activity are gener
ated using AChartEngine library, which allows making charts on Android-based
85
and “Manual Control Node Variables” .
Open CADCE (c) 2017 by Nahitt Padilla
C h o o s e y o u r m o d e ;
SYSTEM PREVIEW
% Q 6:45
AUTOMATIC CONTROL
NODE OBSERVATION AND CONTROL
Figure 55: Application Main Activity.
@pOD©a©i SMfife ©te;€
CONTROL STATUS
CONNECTIONS OVERVIEW CONTROL SCHEMAS
GEOGRAPHIC INFORMATION
(a) Activity System Preview. (b) Activity Automatic Control.
Figure 56: Server Access Services Activities.
devices. This interface is configurable to display multiple kinds of variables avail
able through the system. For example, in Figure 58a the activity is displaying
information captured from the accelerometers. A line plot shows the history of
the readings in three-axis.
Another example is displayed in Figure 58b where the generated voltages are
shown. This instrumentation interface has four buttons. One is dedicated to
controlling the kind of chart used to display the sensor data. Other to change the
displayed information about a specific variable. Other to switch between plot and
only numeric representations, and the last one to define data logging preferences.
$ S an Y sid ro
«v» \ A■? \ X \
I ' ' - X - ' - i ; ' _• \\ .. . y ■, ;L a s C ru c e s
clln A A " " \t .(342)- . „
University,'
\\T n r t im a e U@M) M esiila T o rtu g as
! Google \
On the other hand, if the CE clicks
on the “manual control node variable”
button of Figure 47 the activity of Fig
ure 59 is displayed. This activity has a
scrollable list of node variables. These
variables could be discrete, painted in
yellow, or continuous, painted in blue.
Each variable is described by its system
name. For the discrete variables, the
option is to turn it on or off. The con
tinuous variables could take any value
between zero and a hundred; this per-
centage is later translated by the seal- Figure 5y. Activity Node observation
ing module of the sensor node. Once an(j Control
the value is established the CE must
have to press the apply change button to transmit the value to the node. Vari-i
able details could be consulted such as symbolic names and scales using the “Var
1V *943 Logitude -106 834022'lode Cocje v e f ACD34 Node Nam e E 8!D D rop8
VIEW NODE VARIABLES
MANUAL CONTROL NODE VARIABLES
System.Section Map: Mesiila Valley
Details” button.
87
Acceleration, VoltageLOG OPTIONS LOG OPTIONS
EDIT VARIABLES • CHANGE CHART SHOW NUMERIC EDIT VARIABLES :; CHANGE CHART SHOW NUMERIC:
.V oltageNow Monitoring: Acceleration
(a) View Node Variable Acceleration. (b) View Node Variable Voltage.
Figure 58: Instrumentation Activities.
3.2.3 Control N ode
The control node software depends extensively on the platform chosen to
deploy the system. For the selection of such devices, one important restric
tion imposed by the initial hydro generator design was that it must be low
cost. Initially, the Arduino was selected as the only source of data acquisition
and control. Arduino has some virtues such as good sampling rate and ease
8 8
of programming in a quasi-C language, but it lacks of computing power and
speed. Also, the data acquisition sample rate is divided among all input chan
nels. To overcome these problems several Arduinos could be used in parallel.
Arduinos are not suited to perform
complex calculations on short times,
i.e., the autonomous control must be
very limited and preferable imple
mented using lookup tables.
Not all systems dynamics could
be captured Using static approaches
such as the look-up tables. For more
complex automatic control, new states
must be calculated in real time. One
solution is to increase the computer
power. A low-cost solution is the Rasp-
berry Pi board. Raspberry Pi has com- Figure 5g. Mamla| Control 0utputs
puter power measured in GHz, not in
MHz like the Arduino, but it lacks analog inputs or outputs. This issue is ad
dressed in various ways, see Subsection 3.2.3.2.
The control node’s low-cost restriction must be eliminated to achieve full au
tonomous control power. Using National Instruments DAQ devices with powerful
Sontinous Output Var Name:' VtIN . / - ;
1 .
0 .0 .1 '
. . . Ob
IOC
Numerical Value 8 2 /1 0 0
APPLY CHANGE . VAR DETAILS j
Discrete Output Var Name: ■
OFF0 .1 .1
ON
Actual Value: 1/1
APPLY CHANGE M VAR DETAILS
2ontinous\Output;Var Name:MIN
1
0_2_1 . | * : - ON
ioo'
Numerical Value:. - - . o ' - 7 /1 0 0
APPLY CHANGE
"...
, VAR DETAILS
Discrete Output Var Name: OFF
0_3_1ON
Actual Value:V
1/1
J r ^ APPLY CHANGE’. .. ; VAR DETAILS
Sontmous Output Var Name: o. Xi ' 1
laptops or the CompactRIO System, maximum performance could be achieved,
but the price will grow considerably in comparison to the low-cost options.
Open CACE implements three possible scenarios: i) Low cost, low computer
power: Arduino. ii) Medium cost, medium computing power: Raspberry Pi. iii)
High cost, high computer power: NI CompactRIO.
This section describes the basic characteristics the software has to have for a
control node to communicate to the framework of the Open CACE tool. In fact,
the suffix name of this work “using heterogeneous controllers” is introduced due
to the frameworks’ ability to be implemented in different running environments.
Later, the implementation of the mentioned scenarios is introduced.
The main general algorithm every control node must run regarding of its com
puter power or implementation details is shown in Figure 60
The first labor of a node it to initialize communication with the server. Then
the node could act into two modes: autonomous or manual; these modes are
mutually exclusive. If the autonomous control is activated, the sensor node will
compute the new state using information from the local and remote sensors. De
pending on the computing power of the deployment platform, this control could
be simple, for the Arduino case, or very complex, using a platform such as Rasp
berry Pi or the CompactRIO. After the estate actualization, the control node
must broadcast its changes to the affected nodes of the system.
For the manual mode, the node must be able to process data requests and
90
i Communication Initialization ;2 w h i l e true d o
3 i f Autonomous Control t h e n
4 request data from other nodes ;5 compute new state ;6 apply state change ;7 broadcast changes to participant nodes ;8 e l s e
9 i f message is read t h e n
10 Parse the message ;11 s w i t c h message type d o
12 c a s e Data Request d o
13 get sensor data ;14 form response message ;15 send response message ;16 c a s e Output update d o
17 extract data from message ;18 send data to actuators ;19 send acknowledgement ;20 c a s e Change Control Mode d o
21 Autonmous Control Received Mode22 e n d
23 e n d
24 e n d ,
Figure 60: Generic Control Node Algorithm.
modification to values from different mobile clients through the centralized server.
The node must implement the protocol of Figure 16. This protocol has explicit
instructions for the node to update the outputs. The node is also responsible for
responding with a well-formed protocol frame to the server with variables values
after a request is received.
91
3.2 .3 .1 A rduino
Arduino is a general purpose microcontroller equipped with analog-to-digital
converter. It has inputs to sense the environment and outputs to interact with
actuators. Arduino has to communicate with the server of Open CACE. Some
Libraries exist for Arduino to support Java such as the JArduino library, see
Appendix C, but it has the limitation of not executing code, just accessing inputs
and outputs from Java code. The node implementation was developed usingi
sockets and precompiled functions to allow runtime customization of the execution
control scheme.
For this project the Arduino Mega was used due to its large number of available
inputs: 16 analog and 54 digital I/O . The principal Arduino’s limiting aspect is
its speed of only 16Mhz. Another limitation is its low memory of only 256 KB. A
picture of the Arduino Mega is displayed in Figure 61a.
Another important impediment on the Arduino is its lack of network connec
tivity. This can be solved by using an external shield like low-cost “WiFly” of
Figure 18 or the more reliable official Arduino WiFi Shield of Figure 61b. Those
shields can be easily mounted leaving many available connections. Figure 61c
shows an application of the WiFi shield and the Arduino Uno.
Since the Arduino’s processing restrictions the implementation must remain as
simple as possible. The protocol could be implemented using a C-like structure,
92
(a) Arduino Mega 2560. (b) Arduino WiFi Shield, (c) Arduino Uno + WiFi.
Figure 61: Arduino.
see Figure 62. With the help of the WiFi library, the control node must open a
socket and then it must establish a connection with the server cluster. The control
node then actively interchange packages with the server. Every time a package
is received a simple decomposition is performed to identify the required action.
Similarly, every output must be structured with specific information about the
message.
A simple lexical analyzer is utilized to process the protocol string. It is im
plemented using a loop with a conditional inside, and it is used to dispatch the
message to its specific module. For the sensor read message, the readinput func
tion from the Arduino will be invoked, then the control node will assemble the
response package, and it will send it back to the server. For the update output
message, the package contains information that identifies the output to be changed
and the new value as well. The control node will execute an analog output on the
specified actuator. Also, the control node will send acknowledges to the involved
components as indicated in protocol sequences of Figures 15 and 20.
The autonomous mode is designed to execute very light control, i.e., simple al
gebraic expressions and user-defined procedures involving standard mathematical
functions relating the local input to the local outputs. This mode on the Arduino
is very limited but, in conjunction with the server’s control module, i t ’s able to
perform operations that will compensate the global behavior while waiting for the
optimal response.
# d e f in e MAXPACKAGES 16
ty p e d e f s tr u c t {
char pacID [ 8 ] ; char S en so rD a ta [ 2 4] ;
} p a c k a g e ;
ty p e d e f s tr u c t {/ / openCACE p r o t o c o l im p l e m e n t a t i o n
c h a r Type [1 6 ]; c h a r V ers [ 4 ] ; c h a r F l a g s [1 2 ]; c h a r SourceN ode [ 1 6 ]; c h a r D e s tin a tio n N o d e [ 1 6]; c h a r SensorN um ber [ 8 ] ; c h a r S ensorT ype [ 4 ]; c h a r NumPacks [ 8 ] ;
! char SequenceN um ber [ 1 2]; char TimeStamp [ 3 2 ]; package D ata [MAXPACKAGES];
} f ra m e , *aFram e;
Figure 62: Arduino implementation of Open CACE Protocol.
94
3.2 .3 .2 R aspberry P i
The Raspberry Pi is a single-board, low-cost, high-performance computer
first developed in the UK by the Raspberry Pi Foundation. The Raspberry Pi 2
is constructed around a quad-core ARM cortex which runs at speeds of 900MHz.
It also has 17 GPIO for connecting with devices.
The Pi 2 has a 10/100 Mbps Ethernet RJ45 connector and an SD card adapter.
To plug in a mouse and a keyboard, the Pi 2 has 4 USB ports and a video output
with HDMI connector. It comes with 1 Gb of RAM.
The newest model is the Raspberry Pi 3, see Figure 63, which is faster. It
runs at a speed of 1.2 GHz, and it has an 802.l ln wireless LAN and Blue
tooth connectivity. It has 40-pin GPIO for communication with external circuits.
One big problem with the two mod
els of the Pi is their lack of ana
log input/output ports. A circuit
could be designed to solve this prob
lem, but many top-of-the-shelf solu
tions are available. The I2C P i’s
port could be used to communicate
with the ADS1115 (16 bits of resolution) or the ADS1015 (12 bits) from
Adafruit. Those boards are available at a price of 15 USD in this site h t t p s :
SW4OC0S
\ f X* ‘
HDmi
Figure 63: Raspberry Pi 3 model B.
95
/ / l e a r n .a d a f r u i t . co m /ad afru it-4 -ch an n e l-ad c -b reak o u ts / .
Another solution is to buy a full board with a set of input/output options like
the one from Pi Plates, see Figure 64. This board could be bought from the site
h t tp : / /p i-p la te s .c o m /d a q c r l / at a price of iust 34 dollars.
This plate has eight analog inputs
and two analog outputs with 2 PWM
channels. One advantage of us
ing this board is that the sellers
offer a set of libraries to interact
with the module from Python eas
ily. With instruction such as val =
round{DAQC,getADC(0,8),l) the Pi
could read a value from the sensor 1,
avoiding thus all the programming ef
fort required to build a custom library.
Another alternative is to use an Ar
duino for the analog input/output and then send this information through the
serial port to the Raspberry Pi, see Figure 65. With this configuration, the Ar
duino plays the dedicated role of an Analog-to-Digital converter. A library of
functions must be implemented to deal with the serial communication between
the platforms.,
96
( f i » 1 ■*••».I » *'#' t 'i i
o f t? a tn
Figure 64: Raspberry Pi Output Plate
from PiPlates. Retrieved June 28, 2017
from web site: http://pi-plates.com/wp-
content/uploads/2014/11 /ppD AQC-
1024x795.jpg.
Additional power is required to supply both boards. Also, an additional USB
cable is required to achieve the serial interface.
The Arduino performs pooling on the
inputs, and then it sends the format
ted data to the Raspberry buffer. The
Open CACE implementation supports
both the Pi-plate and the Arduino in
serial communication with the Pi by
providing entry points on the frame-Figure 65: Raspberry Pi and Arduino.
work to customize the input/output
function calls.:
When this project began, the Raspberry Pi 2 was the standard. To fix its main
deficiency, a WiFi dongle is required, see Figure 66a. With this device, the Pi 2 is
ready to communicate wirelessly with the system. Such a device could be acquired
from www.ebay.com at 9.00 dollars the piece. The Pi 3 has a WiFi included as
mentioned before, so it is ready to use. To write the communication protocol the
standard Python socket library was used. Python has a very expressive way to
deal with strings and protocols. The code of Figure 67 shows the Open CASE
protocol implementation used for the Raspberry Pi node implementation.
97
(a) USB WiFi Card. (b) Raspberry Pi + USB WiFi Card.
Figure 66: Wifi USB Dongle.
I
3.2.3.3 N ational Instrum ents Hardware
National Instruments had invested millions of dollars and thousands of hours
into the development of an integrated system that allows the software realization
of industrial automation. NI has achieved that with a line of hardware products
that allows data acquisition and control. Additionally, NI has its development en
vironment, LabVIEW, a de facto standard that allows the graphical programming
of such systems.
The only drawback of NI is the cost; the programming environment LabVIEW
oscillates between 4000 and 6000 USD. Add to that all the required hardware and
the price skyrockets.
The Open CACE environment is in direct competition with National Instru
ments approach, both try to achieve similar results, but the Open CACE platform
is based on low-cost open components and free software packages.
98
Still, it is important for our system to be compatible with the industry stan
dard. In this section, the LabVIEW Node Implementation is described.
from s t r u c t im p o rt * im p o rt d a t e t i m e
# a c u t a l s e n s o r datas i = ’TYPE’ . e n c o d e ( ’u t f - 8 ’ )s2 = ’VERS’ . e n c o d e ( ’u t f - 8 ’ )s3 = ’FLAGS ’ . encode ( ’ u t f —8 ’ )s4 = ’ SOURCE JNfODE’ . encode ( ’ u t f - 8 ’ )s5. = ’DESTINATIONJtfODE’ . encode ( ’ u t f - 8 ’ )s6 = ’SENNUM’ . e n c o d e ( ’u t f - 8 ’ )s 7 ' = ’STYPE’ .en co d e ( ’ u t f - 8 ’ )s8 = ’NPACKS’ . encode ( ’u t f —8 ’ )s9 = ’SEQNUM’ • encode ( ’ u t f —8 ’ )slO = d a t e t i m e . d a t e t i m e ,now() . s t r f t i m e ( ”%Y—%n-%L%
H:%M:%S” ) . e n c o d e ( ’ u t f - 8 ’ )
sphck = p a c k ( ” 16s 4 s 12 s 16 s 16 s 8 s 4 s 8 s 12 s 3 2 s ” , s i , s2 , s3 , s4 , s5 , s6 , s7 , s8 , s9 , s l O )
p r i n t ( s p a c k )
^ u n p a c k i n g example from c o l l e c t i o n s im p o r t n amedt up le P r o t o c o l F r a m e = na me dt up le ( ’P r o t o c o l F r a m e t y p e ,
t vers , f l ags , sou rcen ode , de s tno de , sensornum , s e n s o r t y p e , numpacks , seqnumber , t i m es ta mp ’ )
r e s u l t = P r o t o c o l F r a m e . _make( u n p a c k ( ’ 16 s 4 s l 2 s l 6 s l 6 s 8 s 4 s 8 s l 2 s 3 2 s ’ , s p a c k ) )
p r i n t ( ” P r o t o c o l F r a m e - = _ % r ” % ( r e s u l t , ) )
Figure 67: Python Implementation of Open CACE Protocol.
Open CACE supports the use of USB DAQ devices, see Figure 68. The main
characteristic of these devices is that they communicate their readings and receive
their outputs via the serial interface of the computer running LabVIEW. NI offers
99
different devices with different resolutions and sampling rates. The higher the
specs the higher the cost, like the device of Figure 68a with a cost of 1200 USD
plus the 4750 USD for the multifunction I/O device. A much cheaper option is
to use low-cost NI devices such as the one of Figure 68b with a cost of a couple
of hundred dollars. Still the programming environment comes apart at a price of
4000 USD.
S3?
(a) NI USB DAQ. Retrieved
June 28, 2017, from NI web site:
http://ww w.ni.com /cm s/im ages /
devzone/tut/06171005.png.
(b) NI Low-Cost USB DAQ. Re
trieved June 28, 2017, from NI
web site: http://www.ni.com /en-
us/support/m odel. usb-6003.html.
Figure 68: NI USB Devices.
One, even more, cheap alternative is to use an Arduino instead of a NI
DAQ with the use of the library Linx that can be found on the next site: p
https://www.labviewmakerhub.com/doku.php?id=dibraries:linx:start.
This library provides a standard interface to the Arduino, i.e., in the same way,
1 0 0
that the NI instruments devices are connected. In fact, the code implemented for
the Open CACE framework is too general that it will work with the Arduino
instead of a NI DAQ Device with only minimum adjustment.
The use of USB DAQ requires a computer to run. Another alternative is to
use the CompcatRIO. Figure 69 show NI CompactRIOan embedded industrial
type controller, see h ttp ://w w w .ni.com /com pactrio /. CompactRIO is able to
interact with different modules that increase its capabilities.
At a price starting at 8000 dollars
plus 2000 for every additional mod
ule, the CompacatRIO is a Linux run
ning machine with different configura
tions ranging from'the 667 MHz to 1.91
GHz and storage up to 16 GB. Labview
with its real-time modules is used to
program the controller and to interact
with sensors and actuators. The same
program used to USB could be config-I
ured differently to be used with the CompactRIO making it fully compatible with
the Open CACE framework.
The protocol’s implementation on Lab VIEW requires the definition of the
cluster of Figure 70. A cluster is a way to represent C-like structures in Lab VIEW.
1 0 1
Figure 69: NI CompactRIO. Retrieved
June 28, 2017, from NI web site: p
http://www.ni.com/cms/images/ p
devzone/pub/Ql09JNL_pagel8_-
figl-jpeg-
S tcpSimpleClient.vi Front Panel on ProtocolLexer.lvproj/My Computer *
File Edit View Project Operate Tools Window Help
□ X
■>> # ® I I 15pt A pplication Font .pD-r M te,
^ . . + . ^ , - P ro tocg |P rgmJe^ j L l | i | i l l l . . , U 4 - , r 4 . X j - ^ - j . j- j . t . 1
4 t t FltAGS*8 VERS FLAGS
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amp
a 12:43:14.879 PM7/27/2017
■cr .> v * ■■“ " T f T '
- . ..
ProtocolLexer,lvproj/M y C o m p u te r | <
Figure 70: Protocol Header in Lab VIEW.
This cluster could be then dynamically casted to a string to be transmitted over a
socket. Figure 71 also the protocol header casting and its visual implementation.
The simple socket cycle of life is depicted in Figure 71 also. It opens the
communication channel. Then transmits the casted protocol header, then finally
closing the connection. Lab VIEW allows the dynamic casting from strings to
clusters making it relatively straightforward to receive a string with the protocol
header and using it later inside a case structure.
3.3 Deployinent and Testing
The Open, CACE system was implemented on two Linux based laptops with
i5 cores and 2 GHz each. These servers were connected to a single router with
i js tcpSimpleClient.vi Block Diagram on ProtocolLexer.lvproj/My Computer *
File Edit View Project Operate Tools Window Help
□ X
< J > _ # 1 ® _I|J[@_ ? S Wq 15pt A pplication F ont |- r ga v .pa T < 0 ^ [ I------1
P ro toco lFram e
jloca lhostj
gTcP11
Protoco llexer.lvpro j/M y C o m p u te r <
Figure 71: Header TCP Transmission in Lab VIEW.
internet access. The intention was to deploy the servers in the same development
environment. Servers provide service to an intranet, i.e., all mobile devices, control
nodes and servers must be on the same network. For the control nodes, three
Arduinos Mega, two Raspberry Pis, one computer with USB NI DAQ and another
computer with NI DAQ internal card were employed for development and testing.
The mobile application was developed on two Mac Book Pros with i5 processors,
and it was deployed on a Nexus 6p Android phone and the Sony Xperia (two
models). The test consisted of using the whole system to simulate a typical
running environment. It was run on a server, a set of control nodes and a set of
mobile devices.
The most complex part of testing is the control node since it has to use different
inputs and outputs. The first setup was to test it on the Arduino platform, using
103
a function generator and a relay board, inputs and outputs were simulated. The
Arduino connected properly through the WiFi with the server sending data from
the sensor. Then the server sent his data to the mobile devices. Also, the mobile
device was used to update the control node’s state. Also, the automatic control
feature was tested by running a simple script. For the next platform, a Raspberry
Pi with a Pi-plate module was used instead of the Arduino in the configuration.
The same signals and actuators were used. The system performed in the same
manner. Next, the Raspberry Pi with an Arduino for DAQ was tested, giving
similar results. Lastly, the NI devices were tested. A PC connected to the network
with a PCI DAQ card was used, providing an excellent result. Then the same PC
but with a USB NI DAQ also passed the test.
104
4 RESULTS
The project suffered a transformation process from a sensor network to a whole
framework supporting intelligent control and distributed optimization. Figure 4
shows the initial areas concerning the system’s development. In contrast Figure
72 shows the latest areas incorporated into the project.
Artificial Intelligence
IntelligentInstrumentation
IntelligentControl
Instrumentation
Control
NetworkedControlSystems
SensorNetworks Networks
Figure 72: Final Knowledge Domain Areas Involved in the Project.
105
The grey section of Figure 72 represents the scope of this project and it means
that the Open CACE framework is on the intersection of the control, networks,
instrumentation and artificial intelligence areas. This is the main result, the cre
ation of an infrastructure that allows the simplification in the creation of complex
distributed optimal control over a range of heterogeneous platforms. Many con
cepts were merged to create robust communication and software models. Those
models captured the essence of the areas displayed on the diagram of Figure 72.
Those models later became the blueprints of the underlying framework’s software
implementation. This development was possible due to an implicit area in the
project: software engineering which is a subset of systems engineering. Software
engineering allows the software development in a controlled manner.I
Another important result achieved by this works is the reduction of the gap
existent between the praxis and theory in the area of distributed control. In Figure
73 two main approaches to control are shown: the mathematical or theoretical
models and the practical implementation of systems. Those sets mean that there
are several mathematical models to explain same number of systems. We know
how to analyze certain type of these models, and to write beautiful equations with
the ability to control them. On the other hand, the practical implementation
of control systems differs a lot from their theoretical counterpart. Just to give
a trivial example of this difference, in theory, variables are refereed x , y , z , . . .
and it is assumed that they are bidirectional, i.e., They can be read or written.
u
Optimal
PracticalImplementation
TheoreticalModel
Figure 73: Gap Between Models and Implementations.
This situation!just can never happen in the real implementation, since inputs are
connected to sensors and outputs to actuators. There is, however, a soft spot
where mathematics perfectly applies to the real world. The infrastructure here
presented aims the gray section of Figure 73 by allowing the user to visually
analyze and build complex mathematical models with almost no effort. Then the
system automatically perform the complex tasks of mapping the mathematical
entities to real devices and to dealing with the transmission and state updates to
the whole operation. Furthermore, optimality has to be proven theoretically, thus
existing only a few of such optimal implementations. Open CACE also allows
the construction pseudo-optimal or optimal-tending systems to achieve this even
107
harder task of implementing an optimal distributed control system.
4.1 Deploym ent R esults
In the more practical sense, not only models and abstractions were created.
Millions of lines of code were developed and typed. The implementation of these
models that intersects knowledge areas are by themselves a big result. This in
frastructure contains primarily the server code, which is the heart and brains of
the operation;. Also, one android version of the mobile observer is contained.
Lastly, the implementation has four different nodes to be deployed on the control
nodes, one for Arduino + WiFi, one for Raspberry Pi + WiFi + P-plate, one
for Raspberry Pi 4- Arduino as DAQ and one main code for the NI platform in
Lab VIEW. For comparison purposes a test was designed and executed between
the Open CACE and Lab VIEW, see Appendix D for results.
108
5 CONCLUSIONS
the implementation of distributed control systems is a very complex task due
to the integration of disparate technologies. The work presented here achieves
this integration with the development of a common running framework installed
on every system’s component. The framework consists of a series of libraries and
a custom communication protocol. The libraries contain functions that provide a
standard Application Program Interface (API) for the creation of more complex
programs. The custom communication protocol implements the required sequence
of steps to transmit information through the control network. The libraries and
the protocol enables a real-time transparent mapping of inputs and outputs to
state variables.
The framework is used to develop three main software components: the server,
the control node, and the mobile client. The server allows the user to create com
plex mathematical models using the mapped state variables. The server auto
matically translates the model into a real world application utilizing the control
nodes. The models supported by the framework include neural networks, fuzzy
logic, greedy optimization, and particle swarm optimization.
The principal innovation of the framework is the ability to apply classic control
methods to the distributed control system. It is possible to compute observability,
controllability, and stability of a distributed networked control system because of
109
the next three main factors: i) The existent mapping of state variables: Sensors
and actuators are used inside equations, automatically updating their values when
changed, ii) The transparent communication implemented by the framework:
Communication allows the use of remote variables, residing in other nodes, like if
they were local to the node, iii) A standard programming API that enables the use
of multiple solvers and functions: The mathematical library has classes to store
vector spaces.1 These classes are used as inputs and outputs of other functions
such as numerical solvers and optimizers. In a nutshell, using the framework’s
combined tools, it is possible to design and implement automatic control schemes
using the visual server interface. One extra feature of the framework is the ability
to model the communication randomness of the underlying network dynamically.
With this model, the framework compensates on the delivery time of control orders
sent to the nodes.1
When the server is running concurrently with a set of control nodes and a set
mobile clients, the framework’s full potential is achieved.
The server presents to the user a visual environment for the development of
automatic control models. Also the server is in charge of running all background
services required for framework to work. Such services include: state variable
mapping, secure user authentication, geographic node location, data logging, and
collection of operation statistics.
The control node software module implements the protocol and API on three
1 1 0
different platforms: Arduino, Raspberry Pi, and NI Lab VIEW. The Arduino ap
plication allows for a low-cost implementation but not a very sophisticated local
control logic, due to its limitation in computing power. The Raspberry Pi and the
CompcatRIO were utilized as control nodes, to increase its computing power. The
framework allows the communication with multiple nodes concurrently, executing
on different platforms.
The framework’s last part is the mobile client. Every component of the system
could be monitored and adjusted using the application running on a mobile device.
Smartphones allow the distributed real-time monitoring of all system inputs. Also,
the mobile client enables the manual adjustment of every system output.
5.1 Future Work
Expand the library of mathematical functions to achieve a more specific and
robust implementation such as the Kalman Estimator for the sensor input process
ing. Another important aspect to improve is the Graphical User Interface(GUI)
to make it more functional. Further development must expand the Interface con
cept to create a true visual language with a well-defined syntax and semantics.
Other functions should be developed on the intelligent control library including
the support of Pulse-Coupled Neural Networks to process sequence of pulses in
time instead of vectors like the traditional neural networks do. One important
aspect to work in the future is to establish the computability limits of the models
that could be executed by the automatic system. The interface allows the cre
ation of multiple models. However, the model size, affected primordially by the
number of variables, could make it uncomputable. Thus, it is crucial to establish
the limits of the software.
1 1 2
APPENDICES
A W A T ER FLO W M E A S U R E M E N T
The generator must adjust its behavior with the change of the water flowing
into it. In order to perform this adjustment, an accurate water flow measurement
must be realized. This section explores the current technologies in the filed of
electronic water flow measurement.
Examples of flow measurement technologies are: coriolis, differential pressure,
magnetic, positive displacement, target, thermal, turbine, ultrasonic, variable
area, vortex.
The obtained measurement could be: Volume flow rate (Volume), Mass flow
rate (Mass), Flow velocity (Velocity).
Some information provided in Appendix A.l was obtained from on-line man
uals of [177]. 1
I
A .l F lu id P re lim in aries
For the instrumentation, many hours of many weeks were dedicated to theI
study of fluids and fluid dynamics. The first concept is the definition of fluid that
is a material that if some stress is applied the fluid will deform creating a flow.
It is assumed some basic knowledge of physical variables such as: pressure p with
units N / m 2. velocity v expressed in m /s, density r in k g /m 3 and viscosity m in
113
terms of kg/(m — s) or also Pascal ■ s, and the obvious time in s (seconds)
Same laws apply to gases and liquids, for mathematical purposes they are both
fluids. The main difference between them is that you can compress gases but not
liquids.
The flow can be classified in either laminar or turbulent depending on how
viscous it is and how they react to inertia. This relation is expressed in the
Reynolds number.
The fluid flow can be modeled using the Navier-Stoke equations which governi
all laminar flows. In the special case of fluid with no viscosity, the BernoulliI
equation could be used to describe the behavior of the flow.
Bernoulli equation
For the Bernoulli equation to work, several facts must be assumed: The flow is
non-turbulent1 also called laminar due to the fact that is modeled with streamlines,I
An streamline is the path of the fluid in a flow. Also this flow must be perfect
that means has zero viscosity. Also the flow must be compressible due to its
change in density, this effect is presented on high velocity flows with speeds of 0.3
Mach, low velocity flows are usually uncompressible. Further more the flow must
be baritropic,: which is the flow whose density and pressure are not temperature
114
Dependant. Then the Bernoulli equation describing a constant fluid flow is:
V 2 P—— I- z H— = C(streamline) (7)29 9
This equation states that the addition of fluid velocity V, pressure P and
head z must remain constant C in along all streamline in a particular flow. This
constant could be different from streamline to streamline. In the case of flows
that can not create loops, called irrotational, The constant C is the same for all
streamlines in the flow. Usually only flows with no viscosity can be irrotational.
For an incompressible fluid the Bernoulli Equation becomes:
V 2 p7)— V z + — — C (8)29 99
A .2 Differential Pressure Fiow-m eters
From the many flow measurement studied to develop the original instrumen
tation it was concluded that the differential flow method was the most common
method to measure flow inside pipelines. Other factor that had influence on the
selection of the differential pressure flow meter is the Venturi shape of the gener
ator which perfectly fits the requirements of that kind of measurement.
In a nutshell, the differential flow uses the fact that the summation of forces
inside the pipe must remain constant, fact derived by the Bernoulli relation in
Equation 8.
115
The working principle of this flow measurement is reducing the value in one
variable of Equation 8, then inferring the flow velocity, e.g., the flows goes thought
a pipe of diameter D and then is forced to pass trough a section of the pipe with
diameter d and D > d but still the summation of its pressure, height and velocity
must remain constant. This algebra is on the Appendix A.3, Combining this
velocity with the pipe diameter the water flow, in m3/s could be then computed.
The main drawback of differential-flow meters is that they generate a change in
pressure, in this particular application , this change is not a problem but a design
feature used to increase the generator’s blade speed. Industrial applications reefer
to this kind of application as Venturi Tube flow measurement
A .3 Differential Flow M eter Formula Derivation
The first important relation to establish is the power generated given a flow
rate: ij
Definition A .l . (Generated Power) Generated Power P by a water flowing
Idown from height h is defined a s P = p- h- Q- g - k , where
• P is the power in watts,recall the definition of a watt W = j = =
= V ■ A;S'5 5
• p is the density of water (~ 1000^);
• h is the height in meters;
116
• Q is the flow rate in cubic meters per second;
• g is the acceleration due to gravity of « 9.8^
• k is the coefficient of efficiency ranging from 0 to 1.
Other very important relation is the Pressure p defined as
P = l W
Prom Newton’s second law F = m • g and knowing that m = volumen • p
Equation 9 becomes
V — h ■ p ■ g (10)
Pressure unit is Pascal, Pa =7 m * n - s z
Water discahrge Q is defined in m3/s as
Q = A V (11)
where A is the cross-sectional area of the portion of the channel occupied by the
flow m 2 and V is the average flow velocity m /s
On on the basis that the water is incompressible, the contiuity of water flow
is stableshed as
Q = AiVy = A 2V2 = . . . = AnVn (12)
Prom classical mechanics, the kinetic energy of a non-rotating object of mass
m traveling at! a speed v is
K e = ^ m v 2 (13)
117
And its unit is the Joule defined as J = = N ■m = Pa -m 3 = W ■ s = C -V
Also Potential energy is the energy stored in an object due to its position
U = m ■ g ■ h (14)
if a point of equilibrium is assumed, i.e. where the potential energy is equal
to the kinetic energy, from 13 and 14
and doing some manipulation
v = y/2 ■ g ■ h (15)
Then substitute 15 on 11 to obtain
Q = Ayj2 ■ g ■ h (16)
At the orifice Flow and due to the physical properties of water a reduction in
the flow is noticed, see Figure 74. Introducing that term to Equation 16
Q = CcA^J 2 - g - h (17)
where Cc ss 0.61
To establish the energy balance flow relationships, from 10 the pressure energy
head can written as
Ph = (18)
P ' 9
118
0.61 A
(a) Orifice with a head of water1 (b) Maximu area of flow
Figure 74: Contraction Coefficient. Retrieved June 9, 2016, from USBR, web site:
h t t p : / /www. usbr.gov/pm ts/hydraulics_lab/pubs/w m m /
Similarly the potential energy head from 14 is!
Uh =
m - g(19)
and the velocity head from 15 is
h =2 g
(20)
All heads are in meters. The total energy head can be calculated as Hi
pressure head + potential energy head + velocity head
Hi — hi + - 2- + ZiY l2 g
(21 )
119
Similarly at another downstream location
H2 — h,2 + + Z2YL2 9
( 2 2 )
Then the total energy balance is then
hi + —-—b Z\ — h2 + ——b Z 2Y i 2 9
(23)
Application of energy equation to measure flow
from the continuity equation it is concluded that V\A\ — V2-A2, and solving
from V2
Substituting 24 in 23, if Z\ — Z2
, VI . V i ( Ai 1+ 2g ~ 2 + 2g \ A 2/
solving for the head difference
' A '
then we can express V2
(24)
(25)
(26)
V,2 _ 1 ~ *2)29
'4 i^2
(27)
1 2 0
Taking the square root of both sides and multiplying by A\
(28)
Rearranging and including the contraction coefficient
Q — CcA iA 2{hi - h2 )2 g
A f - A l(29)
A .4 ISO 5 l6 7 Derivation
The International Standard Organization standardized in 2003 in the ISO
5167 standard named “measurement of fluid flow by means of pressure differential
devices inserted in circular cross-section conduits running full” the equation used
in instrumenting venturi type tubes. Derivation of this formula is presented here
to compare with Equation 29 used for this project.
Bernoulli equation states:
The first term p ■ g ■ h is the potential energy, If a constant height is assumed
Const = p- g- h + - p - v 2 + P (30)
where p is the fluid density, v is the linear velocity of the fluid element and P is
pressure.
121
this term can be discarded.
Cost = \ -p -v 2 + P (31)64
here the term | p-v 2 represents the kinetic energy with density replacing mass.
Applying this equation to a circular cross-section pipe that is reduced in di
ameter as it goes downstream in horizontal direction, see Figure 75
Figure 75: Pipeline Cross Section.
-p i ■ v\ + Pi — - p 2 ■ vl + P2 (32)
Mass is conserved (nor created nor destroyed) as it flows through the pipe
Qm = P2 • v2 ■ A 2 = pi • vi ■ Ai (33)
Qm is the mass flow rate along pipe, units kg/sec. Squaring both sides of 33
122
and solving for v%
2 I Pi ' ^12
* = (34)
plug it in 32
2 ■ (-Pi — -P2) = p2 - v l ~ Pi- v\ (35)
then substituting v2 from 34 into 35
o (p n \ _ 2 [ 2 / P l ' ^ l V 2 | ,2 P2 ' (Pi ’ A l f ~ Pi ‘ (P2 ‘ A?)2
(36)
from 36 iq, can be written as
«i = \ /2 • (P, - P2) ■ , j — (pl • A i * -------------------------(37)V P2 ■ (p i • ^ i ) - p i • (P2 • Mr
this value is substituted on Equation 33
Q m = P i ' - t U = P i ■ (Pi - P2) • I 2 . (P' AA ' f {P\ ' „ (38)V p2 • (p i • Mr - pi • (P2 ■ Mr
Since the pipes are circular with D upstream diameter and d downstream
diameter. Their circular cross areas are: A 2 — it ■ {d/2)2 and A x — n ■ {D/2)2.
123
After substituting on Equation 38 and ordering terms:
« « = J n T Z p ' i ' d2 ' %/2 ' <p i - p » ) ' P' <39)
with j3 = jj. This equation was only obtained from Bernoulli and mass con
servation. It is very similar to equation (1) on page 6 of ISO 5167-1:1991(E)
document. In fact for uncompressible fluid, & = 1, it gets even more similar.
For more information the reader can consult the standard itself at:
ISO 5167-4:2003(en) Measurement of fluid flow by means of pressure dif
ferential deviqes inserted in circular cross-section conduits running full h t tp s :
/ / www.iso. o rg /o b p /u i/# is o : s t d : i s o :5 1 6 7 :-4 :e d -1 :v l :en
124
B INSTR U M EN TA TIO N IM PLEM ENTATION
This section presents design components generated during the original hydro
generator instrumentation. Such components are the signal conditioning circuit,
the scale formulas sued for the inputs and the labview code for the Virtual Instru
ment.
B .l Printed Circuit Board
The signal conditioning board encloses circuits from Figures 9, 11 and 12
into an single 'component with an interface which is easily connected to the DAQ
selected in this project. The schematic of the signal conditioning bard is shown
in Figure 76. This board has four I/O ports J 1, J 2, J 4 and J 5 which powers
the broad and send the amplified signals to the data acquisition segment. Thisi
port has two three-axial acceleration outputs. Also has two bipolar temperature
inputs connected to a Wheatstone bridge then to the instrumentation amplifier
providing temperature outputs in the range of the DAQ component.
It has the 'hall effect connector with it respective output. Also provides the[
current and voltage inputs for measuring generated power.
125
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126
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.
Figure 77 shows the final board before they were sent to the manufacturer. A
batch of five such tables were produced.
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•-EKJ-* •-EE}-*•-Ea*# ♦-E2>*
w
HyPER I n s t r u m e n t a t io n
HMSU
(a) PCB Silkscreen. (b) PCB Tablet.
Figure 77: Final PCB for Signal Conditioning.
B.2 Sensor Scales
After the signal conditioning phase, all outputs range from 0 to 5 volts. I t ’s
then required to scale the output to properly fit the sensor response. Every sensor
has a response table, provided by the constructor, that can be used to scale.
Unfortunately most of them the are not lineal at the extremes of operation. In
127
order to simplify the scale calculations a lineal scale is preferred. All sensors were
selected with a good lineal response on the region of interest, so no data is lost
during the scaling. All scales are done in software.
Pressure Sensor
A linear response is assumed in the next interval: 0.5 Volt corresponds
to 0 PSI and 4.5 Volts to 30 PSI. In Figure 78 a plot of the sensor response
to the change in pressure is presented. So the line response is given by the
points (0,0.5); and (30,4.5). The line equation is Y = m x + b with m = (y2 —
yl) / (x2 — x l ) = (4.50.5)/(300) = 2/15. Therefore the offset value is b = 0.5.
The scale equation is Volts =
(2/15) * P S I + 0.5 If Volts
are given the scale can be
expressed as (Volts — 0.5) *
(15/2) = PSI , |
£
Acceleration Sensor
For the accelerometer the
operation ranges is: 1.5 VoltsI
— 0 G and the change rate in pjgure yg- Linearized Pressure Sensor Response,
gravities is of 300 mV — 1 G.
128
Pressure Scale
4
3
2
1
0 5 10 15 20 25 30
Figure 79 shows the sensor response to the change in acceleration. So the line
equation using the points (-5,0) and (5,3) is Y = m x + b with m = (y2 — y l ) / (x2 —
x l ) = (30)/(5 — (—5)) = 3/10. So b = 1.5 The scale equation is Volts = (3/10) *
Gs + 1.5 And given the volts the scale becomes (Volts — 1.5) * (10/3) = Gs.
Voltage M easurem ent
This section presents data on voltage monitoring output. Table 1 shows the
values obtained after a high-swing test.
Figure 80 plots the sensor
response to the change in volt
age. So the line equation using
the points (—240,0.281) and
(240,4.719) is Y = mx+b with
m = (y2 — y l ) / (x2 — x l) =
(4.7190.281)/(240 — (—240)) =
0.009245833. 1 Therefore b =
2.5. The scale equation is
Vout = (0.009245833) * Vin +
2.5. And if given the Vout the
scale becomes (Vout — 2.5) * (1/0.009245833) = Vin
Table 1 also shows the values obtained after a low-swing test. In figure 81 the
129
Acceleration Scale
3
2
1
0
6 2 0 2 64 4Gravities
Figure 79: Linearized Accelerometer Response.
Table 1: Voltage Scale.
Nom-High swing Nom-Low swing
V,n (V) 240 -240 24 -42
V o u t swing(V) 4.719 0.281 2.888 2.112
Generator Speed Test for Voltage Sensor Calibration.
sensor response to the change in voltage is plotted.
So the line equation us
ing the points1 (-42,2.112) and
(42,2.888) is Y = m x + b
with m = (y2 — y l ) / (x2 —
xl ) = (2.8882.112)/(42 -
(-42)) = 0.009238095. So
b — 2.5. The scale equation is
Vout = (0.009238095) * Vin +
2.5. And given the Vout the
scale becomes (Vout - 2.5) *
(1/0.009238095) - Vin.
Voltage Scale
5
4
3
2
1
0300 -200 -100 0 100 200 300
Vin
Figure 80: Linearized Voltage Response Nom-
High Swing.
130
Current Sensor
For AC current the ratio is divided as 0-5V corresponding to 0-150A of current.
Figure 82 graphically shows
the sensor response to the
change in current. So the line
equation using the points (0,0)
and (5,150) is Y = m x + 6
with m = (y2 — y l ) / (x2 —
x l ) = (1500)1/(50) = 30, so
6 = 0. The scale equation is
Current = (30) * V in
B.3 Lab V IEW Code
Voltage Scale
5
4
3
2
1
0 1- ...................................................
-300 -200 -100 0 100 200 300Vin
Figure 81: Linearized Voltage Response Nom-
Low Swing.
The original stand-alone
implementation of the hydro
generator instrumentation is described here. This virtual instrument is displayedi
in Figure 83. Basically it is in infinite loop that takes both pressure measurements
and the it computes the water velocity to calculate next the water discharge. This
basic GUI has no external communication with any other program and it is de
signed to monitor the energy harvester while connected to a USB NI-DAQ or an
131
arduino working as DAQ. This Lab VIEW program allows the data logging and
in conjunction with the interface of Figure 14 is used to monitor all the system
response.
Current Scale
150
100
0 2 3 41 5Vin
Figure 82: Linearized Current Response.
132
133
Figu
re
83:
Lab
VIEW
In
stru
men
tatio
n C
ode.
C EX TER N A L SOFTW ARE LIBRARIES
This part a list of libraries developed by other companies or programmers
is presented. These libraries influenced the development of this project either by
using some code or borrowing some implementation aspects. When code was used,
proper care was taken in not violating any copyright. The name of the package is
provided next to a link to its web page.
• Open Source Fuzzy Logic Library and FCL language implementation: Used
a the base of the fuzzy logic package of this project.
h t t p :/ / j fu z z y lo g ic . so u rc e fo rg e .n e t/h tm l/jav a .h tm l
• Neuroph, java neural network framework: Used to implement the neural
network training algorithms.
http://neuroph.sourceforge.net/
• Java genetic Algorithms Package: Used to implement the genetic and evo
lutive optimization section of this project
h ttp : / / j gap. sourcef orge.net/I
• Open Source Swarm Optimization Package: The PSO code of this project
is based on this library
h ttp : / / j swarm-pso. sourceforge.net/
134
• JavaOctave: This library allows to run Octave scripts from java, it was used
to enable the matlab based control definition in the software.
h t t p s : / / k e n a i . com/proj e c t s / j avaoctave /pages /H om e
• Java Universal Network/graph Framework: This library is the base of all
graphical graph/network representations used in the GUIs. The node selec
tion interface, and the control design relay heavily in this package.
h t t p : / / j u n g . s o u r c e f o r g e . n e t /
• JFreeChart: A library to plot graphics, heavily used in the node instrumen
tation visualization on the server.
h t t p : / / www. j f r e e . o r g / j f r e e c h a r t /
• GoogleMaps API: This library was used in the server and mobile client, and
allows the creation of interactive maps in real time.
h t t p s : / / d e v e l o p e r s . g o o g l e . com/maps/
• JScience, java tools and libraries for the advancement of sciences: It contains
a set of mappings to mathematical structures such as rings and fields. Also
has a matrix template to solve linear algebra.
h t t p : / / j s c i e n c e . o r g /
• Apache Math: This library contains many useful classes to solve problems
that range from ODE to machine learning.
135
h t t p : / / commons. a p a c h e . org/p roper /commons-m ath/
• JavaCalculus: Allows the program to parse algebraic expression from strings
h t t p s : / / c o d e . g o o g l e . c o m / a r c h i v e / p / j a v a c a l c u i u s /
• Michael Thomas Flanagan’s Java Scientific Library: Some of its functions
to solve control systems are used in the system’s math library.
h t t p s : / /www. e e . u c l . a c . u k / ~ m f l a n a g a / j a v a /
• AChartEngine: It is a library to make charts on Android based devices. It
is heavily used on the mobile client in the instrumentation section.
h t t p :/ /www. a c h a r t e n g i n e . o r g /
• JArduino: Allows the reading and writing of inputs to a serial communicated
arduino from a java program, it has the limitation of not executing code,
just accessing inputs and outputs.
h t t p s : / /g i th u b .c o m /S I N T E F - 9 0 1 2 /J A r d u in o /w ik i
I
• Mikhail Gorodetsky Python’s packing and unpacking library: Used in the
protocol production for the raaspberry pi pythno client.i
h t t p s : / / p a s t e b i n . com/XJyZMyHX
136
D SYSTEM PER FO R M A N C E COM PARISON
To compare the system developed in this dissertation with the commercial
top-of-the-shelf software the next hypothetical scenario is supposed. There are
two lighthouses, one in the New York coast and other in Los Angeles. Figure 84
depicts the distance of about four thousand Km between the two points. Each
Lighthouse has a turning mirror that rotates at variables speeds over a bright
incandescent light. The mirror turns thanks to a motor that has hall-effect speed
encoder.
Lighthouse
Lighthouse
Figure 84: Lighthouses in the East and West Coast.
The motor on NY is running at full speed while the one in LA is stalled. The
idea is to make them spin at the same rate automatically. This coordination is
137
achieved because each lighthouse has an identical node comprised of: i) A motor,
ii) A tachometer attached to the motor shaft, iii) A Micro-controller with the next
abilities: it can acquire the speed sensor data, and it can adjust the actual motor
speed to achieve the desired speed reference. Figure 85 depicts the lighthouse’s
control node.
SpeedAdjustment
Speed.ererenceDAQ and Control
Figure 85: Control of Lighthouse Mirror Motor.
i
D .l Control A rchitecture
To achieve coordination among the lighthouses’ motor speeds the next se
quence of events must occur. Each control node must send its current speed to
the other node and receive the other node’s speed, then use the received infor
mation to adjust its own. The other control node must transmit and receive the
138
same information simultaneously. This configuration is depicted in Figure 86.
This architecture shows the main problem in distributed control: communication
between nodes. Communication is problematic for the next reason: if the LA
node is running a zero velocity, this is the information that will be sent to the
NY node. NY node also will send the full speed to the LA node. These mes
sages could reach their destination at different times, but once they have arrived,
they affect the speed of its receiving node. Every node is supposed to send its
speed to the other at a constant rate no matter if it has received a new update
or not. T h is1 phenomenon implies that for several moments during the control
process, the node will send a state and immediately it will modify its state with
newly received information, thus making the sent information inaccurate. These
discrepancies make harder to coordinate the lighthouses’ motor speeds accurately.
D.2 Test Description
A test to compare Lab VIEW with Open CACE was designed in the next
form: two simulated nodes running a motor model with its speed control are
connected through a local network with added random latency. To simulate the
latency from LA to NY information provided from the next AT&T site was used:
h t t p : / / ipnetw ork . bgtmo. i p . a t t . net/pw s/ netw ork_delay. h tm l. A simulated
normal distributed latency of 60 ms average with a standard deviation of 20 ms
was used in the test.
139
SpeedAdjustment
SpeedReference
DAQ and Control
Los Angeles
l auJsVrSpeed
Adjustment
reed
Speed DAQ and Control k J
Reference
New York
Figure 86: Simple Distributed Control.
D .3 Lab VIEW Im plem entation
Lab VIEW offers at least three methods for implementing distributed con
trol systems: i) TCP/U D P Sockets, ii) The DataSocket Transfer Protocol, iii)
The Distributed Control and Automation framework. The TCP/U D P Socket is
the most low-level form of communication implemented by NI. It consists of Vir
tual Instruments (VI) that visually implement the socket API in LabVIEW. This
Imethod is the fastest in LabVIEW, but it is also the most complex to implement[
since all the bookkeeping effort is delegated to the programmer.
The DataSocket Transfer Protocol (DSTP) is an encapsulation of the simple
socket. It allows a simpler communication between VI by hiding from the pro
grammer all variable casting details. This method puts an additional execution
140
overhead in the program, making it slower than the simple Socket approach.
These first two methods are too complex to implement leaving all the ab
straction of the controller tasks, variable mapping, and updating to the program
mer. To reduce this complexity, NI has The Distributed Control and Automation
Framework (DCAF). Is against the DCAF that the comparative test is performed
due to its similar approach to variable mapping, and variable auto update.
To set up a distributed control using DCAF in LabVIEW the user must create
a configuration file describing the system using the interface of Figure 87. This
interface allows the mapping creation of measured variables. Also, this interface!
sets up the control logic used by every node. Every control node, or in the DCAF
lingo: standard engine, could be configured with a specific IP address to access
distant nodes through the Internet.
Figure 87 shows the DCAD configuration of a simulation consisted of three
engines, one for each lighthouse motor, and a principal monitoring station to
display the results to the user. Running a distributed control has to implement
all the engines and to put them in a loop, this is depicted in Figure 88. Additional
VI are required for every engine implementing the control of the motor, also a VI
for the visual user interface. Each engine must be in different VI files.
The programmer must define a VI that allows the graphical display of the
system properties. Then the monitor interface must be hooked up to the stan
dard engine (UI) inside the DCAF configuration interface. After that, the User
141
{ 3 SimulatedSystem.pcfg
File Tools About
B t l3 System Configuration 3 © PC
3 0 Standard Engine (UI) ; -j:::Tags : Mappings• |Q Ul Reference ' 9.M UOP
S 0 Standard Engine (Simulation) iS iiiT ags
: &S&K99*• (35 Motor Controller Logic : - J Motor Model; US UOP
: & 0 Standard Engine Tags
■ Mappings i IjScRIO
Manual Mapping ; Visualization ; Automated Interface Matching
Channel Module a Tags | aTemperature Motor Controller li output range high Motor Controller Li output range low Motor Controller Ic
FanLampSetpointP
Thermocouple Rea/ Motor Model Temperature i UDP
1
| .
V !
Tag Dir Channel Module a
Setpoint >> setpoint Motor ControlP » Kc Motor Control1 »> Ti Motor Control0 . » Td Motor ControlFan on? ; > > Fan on? Motor ControlFan << fan Motor ControlLamp << . lamp Motor ControlFan » Fan PWM Motor ModelLamp . » Lamp PWM Motor Model0 . «< . D ' UDPFan on? < < Fan on? UOP1 i << : 1 UOPlam p ' >> lam p UOPP : « p UOPSetpoint << Setpoint • UDP
Figure 87: LabVIEW DCAF Configuration.I
Interface (UI) must be placed in the view section of the program of Figure 88
allowing the centralized monitoring of the distributed control system. This UI is
shown in Figure 91.
D .4 Open CACE Im plem entation
A simple' mathematical model for the lighthouse motor control is shown in
Figure 89. The transparent mapping of inputs and outputs implemented by the
open CACE framework allows the simple creation of a graph that represents the
system. Figure 90 shows the control model for the lighthouse problem. Every
square on the model is directly implemented as a custom function executed by
Ithe node.
The other node in LA will run exactly the same program only switching NY to
142
Pams imi Liy'iUiouJC Mtrts;S I SimulwcrfSw
Fite Edit VimFite Edit View Project Operate Toob Window Help
;#[ii| i TSpt Application font
DCAF Editor
30 System Co£r Ci Stem
s Host Matn.vi Stock Diagram on Lighthouse Motor Controller Testtvproj/My Computer
File Edit View Project Operate Toob Window Help
: ^ ] M ] ( ^ ] Uu 'q* XJ r. I5pt Application font - J ' ? a ’"](-Cn*"j
: ’ K JU 1 0 Ci Stemt : j r T,
’ : ‘ r-fit hr
N
: K JU j 6 G Stem
L j r T.1 ; Shi- 1 3 cRJO
Open end initialize the onftguration for the
specified target alias. [Monitor engine state}
[Check for errors! f* Stopped"
|Close everything!
n the engines I
Thu VI automatically opens when run and the Ul Reference module running mithin the engine will interact with H.
E l i ---------
file nameBuSer
target alias
ED—
This VI contains all of the engine's dependencies. This VI can be updated automatically in the 'Target'
of the Configuration Editor.
Figure 88: DCAF Main Test Module.
LA on the variable description. Both programs are assumed to be inside an infinite
control loop. To achieve this two nodes are activated inside the data layer, and a
random latency is specified. Each node automatically maps its inputs and outputs
to state variables. Custom names could be specified into an XML configuration
file. Then on the control layer, the logic is specified. With a click on the screen,
the framework presents a list of available variables. After placing the variable
in the model space, the user could also select a custom bitmap to represent the
variable. With another click on the screen, the user places variables and custom
functions to operate the controller. Finally, variable flows must be specified by
connecting elements on the screen. Figure 90 shows the implementation of the
143
Error
LA(t)
NY(t ) Control Dynamics
Figure 89: Mathematical Model of the Lighthouse Problem.
lighthouse problem in the Open CACE framework.
After defining the data layer and the control layer, it is necessary to specify
how the node will visualize the system behavior. To do this, a double-click on the
node over the map allows the adjustment of visualization panes. For this test only
one line plot was wired to the system variable of the local motor speed, this can
be seen in Figure 92. The speed behavior tends to the half velocity point stably.
D.5 Test Results
The first test was done using LabVIEW and DCAF. The three engines were
started: two control nodes and the main monitoring UI. The execution result is
shown in Figure 91, and the data log is presented in Table 2.
The second part of the test consisted of the execution of the same control in
the Open CACE environment. The instrumentation GUI of the test is shown in
Figure 92. Also, its log is included in Table 2.
144
I
[j> | Control Design GUI, - □ X
Control Graph . .................. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........................................ . . . . . . . . . . . . . . .A ; : Node Specification:
; j Selected Node:
I L A
i ! I ° ..................................... I
: ; ; Type: . . . . . . . . . . . . . . . . -- • :
i O In P u t Variable |
( 3 I il ® Output variable
i j ; ; 0 Integrator
Error -— __^ControlDynamics \ I\ 0 Derivator
: ! Q Summation
' 1 2 ! * ; 0 Substraction !
I j ; O Constant
'■ \\ O Custom
Q 1N Y
i , ------------------------j j j E d it Details |
0 \ - Control Schemas: •
| ; | Neural |
<... ............. ............. ....................................... .................. . ......... . ......_________ | | | Fuzzy |
System Operation: --------- - .............. - .......... ............ - .......... ............... --------------------- !| ! | Custom | i
1 Stability Analysis Simulate 1 .. Run - ,1 v Optimization. ; :•
— .... . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........................................................................................ *
Figure 90: Open CACE implementation of the Lighthouse Problem.
Data logging was specified to occur every one-tenth of a second. The test run
for five seconds providing fifty samples. A toe-to-toe plot of both execution results
is presented in Figure 93. It is not clear from the simple visual plot inspection
what system is performing better than the other. Nevertheless, smaller and more
compact oscillations could be observed in the OpenCACE execution trace. To
establish if there is any statistical difference between the two signals a student
t-test was performed.
145
a lig h th o u s e M o to r Speed GUI Vt.vi
File Edit V iew Project O perate Tools W in d o w Help
□
" • H * i ® i n
w v "f*» | 5 ,^ )
IlkihtHouseiMotoftStiTiuiatjon
\swmmTimeTSfo'flftw**m
Figure 91: LabVIEW Simulation of the Lighthouse Motor Distributed Control.
D .6 Student’s t-test
This section presents the procedure of the Student’s t-test. This test is used
for comparing the means of two samples (see Table 2), in simple terms, the t-
test compares the actual difference between two means with the data variation,
represented by the standard deviation of the difference between the means.
The first sample is the time, in seconds, of the data acquisition. The second
146
Instrum entation GUI for N o d e : 1ACD34
File Edit M easurem ent Panel
Motor Speed: • -
Motor Speed
Sec.
Speed:
.55
3 .0 3 .5 5 .0
Motor Speed]
Figure 92: Open CACE Simulation of the Lighthouse Motor Distributed Control.
sample is the motor velocity of the LabVIEW implementation, and the third one
is the Open CACE motor speed. The experiments are paired, i.e., both executions
were realized at the same time interval.
Procedure
1. Construct a null hypothesis: A suitable null hypothesis is that there is no
significative difference between the two execution speeds.
2. List the data: The data obtained after the paired experiment is shown in
Table 2.
3. Record the number of replicates: rij = 51 and n2 = 51. Note that the
147
Table 2: Simulation Results.
X y l y2 X yl y2 X yi y2
0 1 1 1.7 0.3 .27 3.4 0.63 .490.1 0.66 0.56 1.8 0.26 .3 3.5 0.6 .430.2 0.18 .17 1.9 0.33 .4 3.6 0.53 .370.3 0.48 .47 2 0.51 .56 3.7 0.49 .440.4 0.6 .59 2.1 0.57 .64 3.8 0.45 .470.5 0.84 .87 2.2 0.6 .7 3.9 0.4 .510.6 0.49 .53 2.3 0.75 .65 4 0.37 .540.7 0.37 .35 2.4 0.69 .60 4.1 0.38 .570.8 0.21 .23 2.5 0.63 .53 4.2 0.42 .530.9 0.33 .35 2.6 0.57 .46 4.3 0.44 .471 0.48 .56 2.7 0.5 .37 4.4 0.48 .44
1.1 0.54 .60 2.8 0.48 .40 4.5 0.52 .401.2 0.72 .63 2.9 0.36 .43 4.6 0.56 .361.3 0.84 .83 3 0.42 .50 4.7 0.59 .411.4 0.72 .70 3.1 0.5 .53 4.8 0.57 .461.5 0.51 .49 3.2 0.53 .57 4.9 0.54 .511.6 0.45 .42 3.3 0.63 .54 5 0.49 .55
Motor Speed Results for the Lighthouse Problem Simulation. The column x is the time in seconds, y l is the motor velocity in LabVIEW and y2 is the motor velocity in Open CACE.
Student’s f-test works even with a different number of replicates.
4. Calculate the mean of each sample: for the LabVIEW it is y\ = 0.5198 and
for the Open CACE y2 — 0.5049 respectively.
5. Estimate the Variance of the populations using the sample: for the sample
one it is a\ = 0.0249 and for sample two a \ = 0.0226. This must be
148
- LabVIEW- Open CACE
0.8
'■ da>cpaco
0.4
0 2 3 41 5Time in seconds
Figure 93: Test Execution Comparison,
represented as S f and Sf respectively since they are samples.
6. Calculate the variance of the difference between two means: This step is
achieved by ad 2 = & + = M2M + o^e, = 0.000931373J a n \ r i2 51 51
7. Calculate ad- Simply take the square root of the previous result ad =
0.030518397
8. Calculate the Lvalue: this vales is obtained as follows t — when doing
this transpose the means so a positive value is always obtained. In this case
0 .5 1 9 8 -0 .5 0 4 90 .0 3 05 1 8 39 7
0.488230101
9. Get the Lvalue: This step is performed by entering the Ltable at (n i+ n 2—2)
degrees of freedom; and choose a significance required (normally p = 0.05)
149
and read the tabulated table. In this case the degrees of freedom are 98 and
the t-value at 0.95 is 1.660
10. Analyze the result: If the calculated t value exceeds the tabulated value it is
said that the null hypothesis was rejected , i.e., the means are significantly
different at that level of probability
This test is enough to ensure that the speed obtained from LabVIEW program
is not statistically different from the speed that takes the Open CACE version at
95 percent leyel of confidence.
D .7 Test Conclusions
The result of the two test not being statistically different indicates a hugeI
bias in the simulation. The pseudo-random number generator initializes the seed
at the beginning of the execution providing the same amount of pseudo-randomI
delay. This determinism in conjunction with the sampling rate creates very similar
signals that display the nature of the simulation. More experiment replicas could
be considered to mitigate a little the effect of pseudo-randomness. The definitive
test is a race between LabVIEW and Open CACE using real motors and real
distances, until the day of more resources the simulated results must serve as a
baseline.
The intuition affirms that Open CACE should be faster than LabVIEW. The
150
main reason why Open CACE should outperform LabVIEW is the fact that Lab
VIEW is an interpreted language heavily based in graphical interfaces while the
framework developed in this dissertation focuses on the minimalistic implementa
tion of the communication, increasing thus its performance. Just the DACF adds
more than five hundred additional modules, and it bases most of its communica
tion on a third party app called Putty.
The point that Open CACE easily won is the ease of use compared to a regular
programming language and even the Visual language of LabVIEW.
151
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