INTELLIGENT CONTROL ON FUZZY LOGIC
Presented by N. Elakiya M.Phil Mathematics
OVERVIEWIntroduction
Problem Statement
Methodology
Applications
Concluding
INTRODUCTIONFuzzy sets were introduced by Zadeh in 1965 to represent
data and information possessing non-statistical uncertainties. It
was specifically designed to mathematically represent
uncertainty and vagueness and to provide formalized tools for
dealing with the imprecision intrinsic to many problems.
Fuzzy Logic is based on the theory of fuzzy sets , which is a
generalization of the classical set theory . Fuzzy Logic can be
considered as an extension of infinite-valued logic in the sense
of cooperate fuzzy sets and fuzzy relations into the system.
HISTORY OF FUZZYIn 1930s Fuzzy or multivalued logic was introduced by Jan
Lukasiewicz, a polish philosopher.
In 1937- Max Black published a paper called “Vagueness: a
exercise in logical analysis”.
In 1965 - Lotfi Zadeh published his famous paper “Fuzzy
Sets”. Zadeh extended the work on possibility theory into a
formal system of mathematical logic, and introduced a new
concept for applying natural language terms.
In 1965- 1975: Zadeh continued to broaden the foundation of fuzzy set
theory
- Fuzzy multistage decision-making
- Fuzzy similarity relations
- Fuzzy Restrictions
- Linguistic Hedges
In 1975: Mamdani, united kingdom developed the first fuzzy logic
controller.
In 1977: Dubois applied fuzzy sets in a comphrensive study of traffic
conditions.
In 1976-1987: Industrial application of fuzzy logic in Japan and Europe.
WHAT IS FUZZY LOGIC?Fuzzy Logic is a superset of conventional (Boolean)
logic that has been extended to handle the concept of partial
truth-values between “completely true” and “Completely false”.
It is based on the idea that human reasoning is
approximate, non-quantitative, and non-binary.
The simplest example is temperature. Usually when you
as someone the temperature they respond with “cool” ,
“warm” , “hot” , “very hot” as opposed to telling the exact
temperature such as “ 28.5 degrees” or “33.1 degrees”.
GENERAL APPROACH TO FUZZY LOGIC CONTOLThe general approaches to designing a fuzzy logic controller is
made up 5 steps:
Define the Input and Output Variables.
Define the subsets(Fuzzy sets) intervals.
Choose the Membership functions
Set the IF-THEN rules
Perform calculations(using Fuzzy Inference) and adjust rules
WHY USE FUZZY LOGIC? Control system requires less information
Can be quicker to implemented
Rules can be tested individually.
Speed and position control in mechatronic systems
Robot trajectory control and obstacle avoidance
control appliances such as washing machine
WHAT ABOUT INTELLIGENT CONTROL?
The premise behind intelligent control is that the system to be
controlled does not have to e rigidly modelled.
This is unlike classical control and biggest distinction between the
two approaches.
Humans can perform complex tasks without knowing exactly how
they do them .Therefore, one may say that an intelligent model solves
* A difficult (non-trivial, complex, complicated) problem.
* In a non-trivial human-like way
TYPES OF INTELLIGENT CONTROLTypes of intelligent control include:
o Fuzzy logic
oArtificial neural networks
oGenetic programming
o Support vector machines
oReinforcement learning
METHODOLOGYFuzzification, Fuzzy Inference, Defuzzification Measured variable Command
Variables (Linguistic Variable) (Linguistic
Variable)
LinguisticLevel
-----------------------------------------------------------------------NumericalLevel
Measured Variable Command Variables
(Numerical Values) (Numerical values)
ITS Manageme
nt
COMPONENTS OF INTELLIGENT SYSTEM
user
user interface
Knowledge engineer Developers interface
Knowledge base
(passive)
Inference engine(active)
Knowledge base manager
(active)
APPLICATIONTRAFFIC MANAGEMNT USING FUZZY LOGIC
CONTROLLER
The inputs regarding the number of vehicles at each participating signal
are obtained through vision sensors.
The number of detected vehicles is sent to the controller which acts as the
brain of the system and produce a unique output for each scenario form
the basis of operation.
Their relations have been defined in the form of “if else” statements in
the fuzzy inference.
Input Fuzzy Membership Functions
Output Fuzzy Membership Functions
Fuzzy Inference System Rules
1. If ( Route-A is light) then (signal-A is +A)
2. If ( Route-A is lighter) then (signal-A is +A)
3. If ( Route-A is high) then (signal-A is +++A)
4. If ( Route-A is medium ) then (signal-A is ++A)
5. If ( Route-A is higher) then (signal-A is ++++A)
6. If ( Route-A is nothing) then (signal-A is null)
7. If (Route-A is light) and (Route-B is few) then (signal-A is +A)
8. If (Route-A is lighter) and (Route-B is fewer) then (signal-A is +A)
9. If (Route-A is high) and (Route-B is more) then (signal-A is ++A)
10. If (Route-A is medium) and (Route-B is much) then (signal-A is +++A)
11. If (Route-A is higher) and (Route-B is numerous) then (signal-A is ++++A)
12. If (Route-A is higher) and (Route-B is few) then (signal-A is +A)
13. If (Route-A is higher) and (Route-B is fewer) then (signal-A is ++A)
14. If (Route-A is higher) and (Route-B is more) then (signal-A is +++A)
15. If (Route-A is medium) and (Route-B is few) then (signal-A is ++A)
The Concept of extension of signal operation time provides longer green light intervals for routes with a greater amount of traffic.
S.No Number of Vehicles Output Time(S)
1 1-3 5
2 4-5 10
3 6 15
4 7 20
PIC16F877A MICROCONTROLLER
The Fuzzy logic Controller is then Followed by the PIC
16F877A Microcontroller which manages the traffic lights according to
the data it receives from the controller.
The main role of the microcontroller is to serially receive and
manipulate the data from the controller and carry out particular action.
Flow chart of overall system working
Traffic Signal Operation
SOME OF APPLICATIONS Aerospace
Chemical Industry
Electronics
Financial
Industrial
Manufacturing
Marine
Medical
Mining and Metal Processing
Robotics
Securities
CONCLUSIONThis presentation has demonstrated Intelligent Control and an improved traffic Controller using fuzzy logic and microcontroller.
THANK YOU
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