Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries

7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries

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EuropeanEmbeddedControl Institute

Graduate School on ControlIndependent Modulesone 21 hours module per week (3 ECTS)

Deadline for ADVANCE REGISTRATION to each module: 20/12/2012

Locations: Supelec (Paris), Istanbul (Turkey), LAquila (Italy), Belgrade (Serbia)

www.eeci-institute.eu/GSC2013

M1

14/01/2013 18/01/2013

Randomized Algorithms for Systems and

Control: Theory and Applications

Roberto Tempo, CNR-IEIIT, Politecnico di Torino, Italy

Fabrizio Dabbene, CNR-IEIIT, Politecnico di Torino, Italy

M2

21/01/2013 25/01/2013

Uncertain Optimization via

Sample-Based Approaches

Marco C. Campi, University of Brescia, Italy

Simone Garatti, Politecnico di Milano DEI, Italy

M3

28/01/2013 01/02/2013Model Predictive Control Eduado F. Camacho, University of Sevilla, Spain

M4

04/02/2013 08/02/2013

The Transverse Function Control Approach

for Highly Nonlinear Systems

Claude Samson, INRIA, France

Pascal Morin, ISIR, Universit Pierre et Marie Curie, France

M5

11/02/2013 15/02/2013

Design and analysis tools for physical

control systems

Antonio Lora, CNRS L2S, Gif-sur-Yvette,France

Elena Panteley, CNRS L2S, Gif-sur-Yvette,France

M6

18/02/2013 22/02/2013

Normal Forms for Nonlinear Control

Systems and Their Applications

Witold Respondek, INSA Rouen, France

M7

25/02/2013 01/03/2013Decentralized and Distributed Control

Giancarlo Ferrari-Trecate, University of Pavia, Italy

Marcello Farina, Politecnico di Milano, Italy

M8

04/03/2013 08/03/2013

Modeling and Control of Automotive and

Aerospace Engines and PowerplantsIlya Kolmanovsky, University of Michigan, USA

M9

11/03/2013 15/03/2013

Stability and Control of Time-delay

Systems

Wim Michiels, K.U. Leuven, Belgium

Silviu-Iulian Niculescu, CNRS L2S, Gif-sur-Yvette,France

M10

11/03/2013 15/03/2013Recent Advances of Sliding Mode Control Vadim I. Utkin, The Ohio State University, USA

M11 - BELGRADE

11/03/2013 15/03/2013

Control of Nonlinear Delay Systems

and PDEsMiroslav Krstic, University of California, San Diego, USA

M12 - BELGRADE

18/03/2013 22/03/2013

Verification and Correct-by-Construction

Synthesis of Control Protocols for

Networked Systems

Richard Murray, California Institute of Technology,USA

Ufuk Topcu, California Institute of Technology, USA

Nok Wongpiromsarn, Singapore-MIT Alliance Research &Tech

M1318/03/2013 22/03/2013

Input saturation: control design andanti-windup

Sophie Tarbouriech, CNRS LAAS, Toulouse,FranceLuca Zaccarian, CNRS LAAS, Toulouse,France

M14

25/03/2013 - 29/03/2013

Traffic modeling and estimation

at the age of smartphones

Alexandre M. Bayen, University of California, Berkeley,USA

Dan Work, University of Illinois at Urbana-Champaign, USA

Christian Claudel, University of Sci. and Tech. Thuwal,KSA

M15

25/03/2013 29/03/2013Model Predictive Control Jan Maciejowski, University of Cambridge, UK

M16

08/04/2013 12/04/2013About Nonlinear Digital Control

Dorothe Normand-Cyrot, CNRS L2S, Gif-sur-Yvette,France

Salvatore Monaco, University of Roma La Sapienza, Italy

M17

22/04/2013 26/04/2013Event-triggered and Self-triggered Control

W.P.M.H. Heemels, Eindhoven Univ. of Tech., Netherlands

Karl-Henrik Johansson, Royal Institute of Tech. Sweden

Paulo Tabuada, University of California at Los Angeles, USA

M18 - ISTANBUL

22/04/2013 26/04/2013

Stochastic Control with Contemporary

Methods and ApplicationsRoger W. Brockett, Harvard School of Eng. Applied Sc., USA

M19 - ISTANBUL

29/04/2013 03/05/2013

Symbolic control design of

Cyber-Physical systems

Maria Domenica Di Benedetto, Universityof LAquila,Italy

Giordano Pola, Universityof LAquila, Italy

Alessandro Borri, IASI-CNR, Rome, Italy

M20

13/05/2013 17/05/2013Nonlinear and Adaptive Control

Alessandro Astolfi, Imperial College, UK

Romeo Ortega, CNRS L2S, Gif-sur-Yvette,France

M21

13/05/2013 17/05/2013Distributed Control A. Stephen Morse, Yale University, USA

M22

20/05/2013 24/05/2013

Extremum Seeking Control:

Analysis and DesignDragan Nesic, University of Melbourne, Australia

M23

20/05/2013 24/05/2013Robust Hybrid Control Systems Ricardo Sanfelice, University of Arizona, USA

M24LAQUILA

20/05/2013 24/05/2013

Optimality, Stabilization, and Feedback

in Nonlinear ControlFrancis Clarke, Universit Claude Bernard Lyon 1, France

M25- LAQUILA27/05/2013 31/05/2013

Modeling and estimation for control Emmanuel Witrant, Univ. Joseph Fourier, GIPSA, Grenoble,France

M26

27/05/2013 31/05/2013Switched Systems and Control Daniel M. Liberzon, University of Illinois, USA

(*) A module will open only if a sufficientnumber of registrations are received before the

advance registration deadline: 20/12/2012


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Roberto TempoCNR-IEIIT, Politecnico di Torino, Italy

http://staff.polito.it/roberto.tempo/

[email protected]

Fabrizio DabbeneCNR-IEIIT, Politecnico di Torino, Italy

http://staff.polito.it/fabrizio.dabbene/

[email protected]

M1

14/01/2013 18/01/2013

Randomized Algorithms for Systems and Control:

Theory and Applications

Abstract of the course

In this course, we provide a perspective of the area of randomization for systems and

control, and study several topics which include the computation of the sample complexity

and the connections with statistical learning theory. In particular, we address system'sanalysis and design using sequential and non-sequential randomized methods, and analyze

advantages and disadvantages of these approaches.

In the second part, we show how randomization is successfully used in several applications

within and outside engineering. We present an overview of these methods for aerospace

and automotive control, hard disk drives, systems biology, congestion control of networks,

quantized, switched and embedded systems, multi-agent consensus. Particular emphasis is

given on the computation of PageRank in Google, web aggregation techniques, and control

design of UAVs. The course is based on the book by R. Tempo, G. Calafiore, F. Dabbene,

Randomized Algorithms for Analysis and Control of Uncertain Systems with Applications,

2nd edition, Springer-Verlag, London, 2012.

Topics: - Uncertain systems

- Probabilistic methods for analysis

- Monte Carlo and Quasi-Monte Carlo algorithms

- Random sampling techniques

- Probabilistic methods for control design- Probability inequalities and statistical learning theory


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Marco C. CampiDepartment of Information Engineering

University of Brescia, Italy

http://www.ing.unibs.it/~campi/

[email protected]

Simone GarattiDipartimento di Elettronica ed Informazione

Politecnico di Milano, Italy

http://home.dei.polimi.it/sgaratti/

[email protected]

M2

21/01/2013 25/01/2013

Uncertain Optimization via Sample-Based

Approaches

Abstract of the course:

Optimization problems involving uncertainty are ubiquitous, and emerge in diverse domains

ranging from control to allocation, from planning to finance. In this course, we shallintroduce the student to sample-based approaches where uncertainty is described by

means of a finite number of samples, or scenarios, coming from the infinite set of possible

uncertainty outcomes. Sample-based approaches represent a viable solution methodology

in a variety of optimization problems involving uncertainty. Samples can as well be

observations, and this covers data-based approaches in learning and identification. A

particular emphasis in the course will be given to the scenarioapproach.

The presentation will be gradual to allow an in-depth understanding of the fundamental

concepts. Special attention will be given to a precise mathematical formulation of theproblems and to a detailed presentation of the ensuing results. Practical examples will

illustrate the ideas.

Topics: - Uncertain optimization

- Monte-Carlo sampling

- Scenario approach

- Applications to various domains

- Discussion of open problems that offer an opportunity for research


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M3

28/01/2013 01/02/2013Model Predictive Control

Abstract of the course:Model Predictive Control (MPC) has developed

considerably in the last decades both in industry and

in academia. Although MPC is considered to be a

mature discipline, the field has still many open

problems and attracts the attention of many

researchers. This courses provides an extensive

review concerning the theoretical and practical

aspects of predictive controllers. It describes the

most commonly used MPC strategies, showing boththe theoretical properties and their practical

implementation issues. As part of the course the

students will program and simulate different MPC

structures. Special focus is made in the control of a

real solar energy plant that will serve as an

application example of the different techniques

reviewed in the course.

The course is designed around the text book:

E. F. Camacho and C. Bordons, Model Predictive Control, 2nd edition, Springer, 2004Prerequisites: Undergraduate-level knowledge of differential equations and control systems.

Topics:

1. Introduction to MPC, process models, disturbance models, prediction equations.

2. MPC used in industry: FIR and step response based MPC. DMC.

3. MPC used in academy: GPC and State Space based MPC.

4. MPC of multivariable processes, dead time problems, choosing the control horizons, MPC

and transmission zeros. Practical aspects for implementing multivariable MPC.

5. MPC and constraints: Handling constraints, QP and LP algorithms. Solving the constrained

MPC, multi-parametric methods. Constrained and stability in MPC.

6. Nonlinear MPC, parametric models, local based function models, optimization methods.

7. Stability and robustness in MPC: Stability guaranteed MPCs, robust stability for MPC,

robust constraint satisfaction, Min-max MPC.

8. Open issues: multi-objective MPC, MPC of hybrid systems, the tracking problem in MPC,

distributed and hierarchical MPC, cooperative MPC.

9. MPC application to a solar power plant: plant models, MPC and intraday market, MPC and

RTO: dynamical optimal set point determination, MPC for set point tracking. Choosing

the appropriate models and horizon for each control level.

Eduardo F. CamachoDept. System Engineering and Automatica

University of Seville , Spain

http://www.esi2.us.es/~eduardo/home_i.html

[email protected]


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Abstract of the course: The course in an introduction to the Transverse Function approach

recently developed by P. Morin and C. Samson to control nonlinear systems that are locally

controllable at equilibria but whose linear approximation is not. Such systems are sometimes

referred to as "critical" systems. The non-existence of asymptotical stabilizers in the form of

continuous pure-state feedback controllers, as pointed out by a Brockett's theorem for a large

subclass of critical systems, calls for the development of control solutions that depart from

"classical" nonlinear control theory. An important motivation for the control engineer arises

from the fact that many physical systems can be modeled as critical systems. Such is the case,

for instance, of nonholonomic mechanical systems (like most mobile vehicles on wheels,

ranging from common car-like vehicles to ondulatory wheeled-snake robots) and of many

underactuated vehicles (like ships, submarines, hovercrafts, blimps). Beyond these theoretical

aspects, an important motivation for the control engineer also arises from the fact that many

physical systems can be modeled as critical systems. Such is the case, for instance, of

nonholonomic mechanical systems (like most mobile vehicles on wheels, ranging fromcommon car-like vehicles to ondulatory wheeled-snake robots) and of many underactuated

vehicles (like ships, submarines, hovercrafts, blimps). Asynchronous electrical motors also

belong to this category.

Pascal MorinUPMC, France

http://www.isir.upmc.fr/?op=view_profil&lang=fr&id=239

[email protected]

Claude SamsonINRIA, France

http://www.inria.fr/personnel/Claude.Samson.fr.html

[email protected]

M5

04/02/2013 08/02/2013

The Transverse Function Control Approach for

Highly Nonlinear Systems

Topics include:

Controllability and stabilization properties of critical systems

Homogeneous approximation of critical controllable systems

Lie group invariance properties of homogeneous drftless systems

Definition, existence and calculation of Transverse Functions

Feedback control design by the Transverse function approach

Application to nonholonomic or underactuated systems


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Antonio LoriaCNRS - France

http://www.lss.supelec.fr/perso/loria/

[email protected]

Elena PanteleyCNRS - France

http://www.lss.supelec.fr/perso/panteley/

[email protected]

M5

11/02/2013 15/02/2013

Design and Analysis Tools for

Physical Control Systems


Departing from the premise that the world is nonlinear, dynamic and deterministic, physicslaws are omnipresent to study the behaviour of systems and their interactions with their

environment. Regardless of the engineering discipline, if Automatic Control is the spine of

technology, Lyapunov stability theory lays at the foundations of model-based control and

qualitative analysis.

This course covers a selected number of tools, useful to analyse the stability and

performance of controlled systems in which physical properties and engineering intuition

are the main steering reins of the control designer. The presentation is streamlined by

particular systems structures such as in the case of Model Reference Adaptive Control,cascaded systems, passive interconnections For pedagogical reasons, particular attention

is put into case-studies stemming from control of robotic systems, consensus, formation

control, electromechanical systems, synchronization, etc.

Topics:

- Stability analysis of time-varying systems, adaptive control, output feedback

- control, robust control, observer design, separation principle ...


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Abstract of the course: The aim of this course is to present a fairly complete list of

normal forms for various classes of nonlinear control systems. Such forms have been

obtained during the last 30 years for various purposes: classification, stabilization,

tracking, motion planning, observation etc. We will attempt to present them in a

systematic way, by providing normal forms, necessary and sufficient conditions for

equivalence to them, and (whenever they exist) algorithmic procedures for obtaining

them. We will show usefulness of the presented forms in various nonlinear control

problems: linearization, flatness, stabilization, output and trajectory tracking, andnonlinear observers.

M6

18/02/2013 22/02/2013

Normal Forms for Nonlinear Control Systems

and Their Applications

Witold RespondekINSA de Rouen, France

http://lmi.insa-rouen.fr/~wresp/

[email protected]

Outline:

1. Feedback and state equivalence.

2. Feedback linearizable systems.

- Globally feedback linearizable systems.

- Partial feedback linearization.3. Special classes of control systems.

- Systems on R2

- Locally simple systems.

4. Triangular forms.

- Lower triangular forms and feedback linearizability.

- p-normal forms.

- Upper triangular forms and feedforward systems.

- Linearizable feedforward systems

5. Formal feedback and formal normal forms.- General systems.

- Feedforward systems.

6. Flatness, dynamic feedback, and normal forms for

subclasses of flat systems

- Normal forms for driftless systems: chained forms.

- Normal forms versus search for flat outputs.

7. Nonlinear control systems with observations.

- Local normal forms.

- Global normal forms


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Giancarlo Ferrari-TrecateDipartimento di Ingegneria Industriale e dellInformazione

Universita' degli Studi di Pavia, Italy

http://sisdin.unipv.it/lab/personale/pers_hp/

ferrari/welcome.html

[email protected]

Marcello FarinaDipartimento di Elettronica ed Informazione

Politecnico di Milano, Italy

http://home.dei.polimi.it/farina

[email protected]

M7

25/02/2013 01/03/2013Decentralized and Distributed Control


Advances in technology and telecommunications are steadily broadening the range and size

of systems that can be controlled. Examples that bring new challenges for control

engineering are smart grids, that are perceived as the future of power generation, andnetworks of sensors and actuators, that enable the monitoring and control of processes

spread over large geographical areas. As an alternative to centralized regulators, that

seldom make sense for large-scale systems, decentralized and distributed approaches to

control have been developed since the seventies. Particular attention has been recently

given to distributed control architectures based on model predictive control that are

capable to cope with physical constraints.

The first part of the course will focus on classical results on stability analysis of large-scale

systems, decentralized control and decentralized controllability issues. Then, distributed

control design methods will be covered. In the last part of the course, more emphasis will

be given to recent advances in distributed control strategies based on optimization and

receding horizon control.

Topics:

- Introduction to large-scale systems and multivariable control

- Decentralized control architectures

- Stability analysis of large-scale systems

- Decentralized controllability issues and design of decentralized control systems

- Design of distributed control systems


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M8

04/03/2013 08/03/2013

Modeling and Control of Automotive and

Aerospace Engines and Powerplants

Abstract of the course:With increasing stringency of fuel efficiency and

emissions requirements, opportunities emerge to

improve engine performance through model-based

control. This course will provide an introduction to

modeling, estimation and control problems for engines

and powerplants in automotive applications, and a

briefer perspective on related problems in aerospace

applications. The use of control-theory based and

model-based approaches will be emphasized.Approaches to handling constraints in engines using

reference governors and model predictive control will

be discussed in detail. The topics covered include

techniques for developing engine control-oriented

models, control and estimation problems for naturally

aspirated and turbocharged gasoline engines, and

modeling and control of diesel engines. Topics of

engine-transmission coordination and energy-

management for Hybrid Electric Vehicles will also be

covered. Related modeling, control and constraint

handling problems for aircraft gas turbine and internal

combustion engines, and for hybrid aircraft powerplant

will also be discussed.

Topics:

1. Basic principles and techniques of engine control-oriented modeling

2. Modeling, estimation and control of naturally aspirated gasoline engines

3. Modeling and control problems for turbocharged gasoline engines4. Modeling and control problems for diesel engines

5. Constraint handling in automotive engines based on reference governors and

model predictive control

6. Engine-transmission coordination

7. Hybrid Electric Vehicle energy management

8. Gas turbine engine modeling and control problems

9. Limit protection for gas turbine engines

10. Hybrid powerplant energy management in aircraft applications

11. Perspective and discussion on control challenges and opportunities for advancedand future engines

IIya KolmanovskyDepartment of Aerospace Engineering

University, of Michigan, USA

http://aerospace.engin.umich.edu/

people/faculty/kolmanovsky/index.html

[email protected]


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M9

11/03/2013 15/03/2013Stability and Control of Time-delay Systems


Time-delays are important components of many systems from engineering, economics and the

life sciences, due to the fact that the transfer of material, energy and information is mostly not

instantaneous. They appear, for instance, as computation and communication lags, they model

transport phenomena and heredity and they arise as feedback delays in control loops. The aim

of this course is to describe fundamental properties of systems subjected to time-delays and to

present an overview of methods and techniques for the analysis and control design. The focus

lies on systems described by functional differential equations and on frequency-domain

techniques, grounded in numerical linear algebra (e.g., eigenvalue computations, matrix

distance problems) and optimization. Several examples (from chemical to mechanical

engineering, from tele-operation to high-speed networks, from biological systems to population

dynamics) complete the presentation.

Wim Michiels

Department of Computer ScienceKU Leuven, Belgium

http://people.cs.kuleuven.be/wim.michiels

[email protected]

Topics:

Theory:

Classification and representation of time-delay systems

Definition and properties of solutions of delay differential equations

Spectral properties of linear time-delay systems

Computational methods:

Stability determining eigenvalues

Stability domains in parameter spaces

Robustness and performance measures

Controller synthesis via eigenvalue optimization

Control design:

Fundamental limitations induced by delays

Fixed-order optimal H-2 and H-infinity controllers Prediction based controllers

Using delays as controller parameters

Silviu Niculescu

Lanoratoire des Signaux et SystmesCNRS - Suplec , France

http://www.lss.supelec.fr/perso/niculescu/

[email protected]


11/27


M10

11/03/2013 15/03/2013Recent Advances of Sliding Mode Control

Abstract of the course:I - Introduction. Mathematical Tools. Design Principles.

The principal design idea of sliding mode control

implies selection of discontinuous control enforcing the

state trajectories to the pre-selected manifold with a

reduced order motion equations and desired

properties of this motion. Mathematical methods for

analysis of differential equations with discontinuous

right-hand parts are surveyed along with their

applications for designing feedback control systems.

IIHigher order sliding mode control

The question of interest whether similar effect can be

reached for the cases with relative degree greater than

one, or when control input is a continuous state

function. Then the range of applications of sliding mode control will be increased. In numerous

publications different design methods for sliding mode control as a continuous state function

were offered and the authors referred to their methods as high order sliding mode control.

The design methods will be discussed in the presentation except for the cases when high order

sliding modes can be easily interpreted in terms of the conventional sliding modes (or first

order sliding modes). The main attention will be paid to the so-called twisting and super-

twisting algorithms.

IIIChattering suppression

Alternative methods of chattering suppression the main obstacle for sliding mode control

implementation - are discussed in this part. As a rule chattering is caused by unmodelled

dynamics. The first recipe is application of asymptotic observers. They serve as a bypass for

high frequency component in control and as a result the unmodelled dynamics are not

excited. However under uncertainty conditions the conventional observers can not be used for

chattering suppression. Another way to reduce chattering implies state-dependent

magnitude of discontinuous control, since the chattering amplitude is a monotonously

increasing function of the discontinuity magnitude. The methodology is not applicable for

widely used electronic power converters with constant magnitude of a discontinuous output.

For these systems the efficient tool to suppress chattering is harmonic cancellation principle.

IV - ApplicationsApplications of the sliding mode control and observation methodology along with chattering

suppression are demonstrated for electric machines, power converters and automotive

engines.

Vadim I. UtkinDepartment of Electrical Engineering

The Ohio State University, USA

http://www2.ece.ohio-state.edu/~utkin/

[email protected]


12/27


M11 - BELGRADE

12/03/2013 16/03/2013Control of Nonlinear Delay Systems and PDEs


In the 1990s, the recursive backstepping design

enabled the creation of adaptive and robust control

algorithms for nonlinear systems with nonlinearities of

unlimited growth and with uncertainties that are not

matched by control.

Taking the backstepping recursion to the continuous

limit provides a design methodology for boundary

control of PDEs and for some key classes of delay

systems. Contrary to standard PDE control that mimics

LQR for finite-dimensional systems and yields virtually

intractable operator Riccati equations, backstepping

feedback laws come with explicit gain formulas. This

course, mostly based on the instructors book

Boundary Control of PDEs: A Course on Backstepping

Designs (SIAM, 2008), teaches how to derive such

formulas for specific classes of PDE systems.

The explicit feedback laws allow the design of

previously inconceivable parameter-adaptive

controllers for PDE and delay systems. Backstepping

also yields the first systematic method for control of

large classes of nonlinear PDEs and for nonlinear

systems with long delays.

Topics:

Lyapunov stability for PDEs; boundary control of parabolic (reaction-advection-diffusion)

PDEs; observers with boundary sensing; wave and beam PDEs; first-order hyperbolic

(transport-dominated) PDEs; systems with input delay and predictor feedback; delay-

robustness of predictor feedback; time-varying input delay; delay-adaptive predictor

feedback; stabilization of nonlinear systems with long input delays; basics of motion

planning for PDEs; system identification and adaptive control of PDEs; introduction to

control of nonlinear PDEs.

Miroslav KrsticDepartment of Mechanical & Aero. Eng.

University of California, San Diego, USA

http://flyingv.ucsd.edu/[email protected]


13/27


M12 - BELGRADE

18/03/2013 22/03/2013

Verification and Correct-by-Construction Synthesis of

Control Protocols for Networked Systems

Richard MurrayControl and Dynamical Systems

California Institute of Technology, USA

http://www.cds.caltech.edu/~murray

[email protected]

Ufuk TopcuControl and Dynamical Systems

California Institute of Technology, USA

http://www.cds.caltech.edu/~utopcu

[email protected]


Increases in fast and inexpensive computing and communications have enabled a new

generation of information-rich control systems that rely on multi-threaded networked

execution, distributed optimization, sensor fusion and protocol stacks in increasinglysophisticated ways. This course will provide working knowledge of a collection of methods

and tools for specifying, designing and verifying control protocols for distributed systems.

We combine methods from computer science (temporal logic, model checking, reactive

synthesis) with those from dynamical systems and control (dynamics, stability, receding

horizon control) to analyze and design partially asynchronous control protocols for

continuous systems. In addition to introducing the mathematical techniques required to

formulate problems and prove properties, we also describe a software toolbox, TuLiP, that is

designed for analyzing and synthesizing hybrid control systems using linear temporal logic

and robust performance specifications

The following topics will be covered in the course:

* Transition systems and automata theory

* Specification of behavior using linear temporal logic

* Algebraic certificates for continuous and hybrid systems

* Approximation of continuous systems using discrete abstractions

* Verification of (asynchronous) control protocols using model checking

* Synthesis of control protocols and receding horizon temporal logic planning

* Case studies in autonomous navigation and vehicle management systems

Nok WongpiromsarnSingapore-MIT Alliance for

Research and Technology,

Singapure

http://smart.mit.edu/


14/27


Luca ZaccarianLAAS-CNRS, Toulouse, France

http://homepages.laas.fr/lzaccari/

[email protected]

M13

18/03/2013 22/03/2013

Input Saturation: Control Design and

Anti-windup


The magnitude of the signal that an actuator can deliver is usually limited by physical or

safety constraints. This limitation can be easily identified in most common devices used in

the process industry, such as proportional valves, heating actuators, power amplifiers, and

electromechanical actuators. Common examples of such limits are the deflection limits in

aircraft actuators, the voltage limits in electrical actuators and the limits on flow volume orrate in hydraulic actuators. While such limits obviously restrict the achievable performance, if

these limits are not treated carefully and if the relevant controllers do not account for them

appropriately, peculiar and pernicious behaviors may be observed (aircraft crashes,

Chernobyl nuclear power station meltdown).

This course addresses stability analysis and stabilization of linear systems subject to control

saturation. We will discuss a first approach consists in designing a (possibly nonlinear)

controller directly accounting for the saturation constraints. Then we will present the so-

called anti-windup approach, where an anti-windup augmentation is inserted on an existingcontrol system which "winds up" (performs undesirably) due to actuator saturation. The anti-

windup feature is then to preserve the predesigned controller before saturation is activated

and to recover stability for larger saturated responses. Anti-windup solutions differ in

architecture and performance achievements. We will discuss several architectures suited for

different saturation problems. Several applications will be used to illustrate the presented

techniques.

Topics: Rate and magnitude saturation, standard and generalized sector conditions, stability

and performance analysis with saturation, linear LMI-based controller and anti-windup

designs, linear and nonlinear model recovery anti-windup design, applications

Sophie TarbouriechLAAS-CNRS, Toulouse, France

http://homepages.laas.fr/tarbour/

[email protected]


15/27


M14

25/03/2013 - 29/03/2013

Traffic Modeling and Estimation

at the Age of Smartphones

Alex Bayen, UC Berkeley, USA, http://lagrange.ce.berkeley.edu/bayen

Christian Claudel, KAUST, Saoudi Arabia, http://www.kaust.edu.sa/academics/faculty/claudel.html

Dan Work, UIUC, USA, https://netfiles.uiuc.edu/dbwork/www/

Sebastien Blandin, IBM Research Singapore, [email protected]

Aude Hofleitner, UC Berkeley, USA and Facebook Inc, http://eecs. berkeley.edu/~aude


The recent emergence of sparsely sampled mobility data has crated new opportunity and

raised challenges for control and estimation problems in intelligent urban networks. The

course presents novel data filtering, modeling, estimation and control algorithms, specific tothe use of smartphone data in the context of transportation and mobility. Specific

implementations from the Mobile Millennium traffic information system will serve as

illustrations for the course.

The following theoretical topics will be covered in the course:

First order flow models: construction of the solution of the Partial Differential Equation

Optimal Control theory for scalar conservation laws and Hamilton-Jacobi equations

Statistical models and graphical networks: Random Markov Fields, Dynamic Bayesian

Networks, Expectation Maximization algorithm Statistical inference in large scale networks: Ensemble Kalman Filter, Particle Filter

Online learning of sparse models

The following applications will be covered in the course:

Real-time traffic estimation on large scale highway and urban networks from crowd-

sourced mobile data

Macroscopic behavioral traffic models on networks

Modeling urban traffic on a network: a hybrid approach of queuing theory and statistical

modeling


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M15

25/03/2013 29/03/2013Model Predictive Control


Model Predictive Control (MPC) is the only

advanced control methodology (ie more advanced

than PID) which has found wide application in the

process industries. It offers advantages which make it

very attractive for other industries too, such as

automotive and aerospace, and its use in such

industries is being actively explored at present. Thecourse will start with the basic ideas of MPC,

together with some specific examples of its

advantages over classical control. It will then

discuss the structure of MPC controllers, present

possible variations (such as non-quadratic cost

functions and stabilised predictions), and deal with

important practicalities, especially disturbance

feedforward and disturbance modelling. A state-

space framework will be used, but the connectionwith the well-known GPC framework will be made.

The course will then survey the state of more advanced MPC-related research, covering

efficient computation, stability and robustness, prioritisation of objectives, the use of

nonlinear models, the application of MPC to hybrid systems (which contain logic or mode

switches as well as continuous dynamics), and distributed MPC. The course will be

illustrated throughout with examples from various applications, including flight control,

spacecraft control, and paper-making.

Topics covered:

1. Basic formulation of MPC

2. Solution of MPC. The GPC formulation.

3. Other formulations of MPC.

4. Stability and tuning of MPC.

5. Robust MPC.

6. Explicit MPC.

7. Case studies & applications.

8. Recent developments & perspectives.

Jan MaciejowskiDepartment of Engineering,

University of Cambridge , UK

ttp://www-control.eng.cam.ac.uk/jmm/[email protected]


17/27


Marie Dorothe Normand-CyrotLaboratoire des Signaux et Systmes

CNRS-Univ.ParisSud-Supelec, Gif-sur-Yvette, France

https://www.l2s.supelec.fr/perso/cyrot

[email protected]

Salvatore MonacoDipartimento di Ingegneria Informatica, Automatica

e Gestionale Antonio Ruberti

Sapienza Universit di Roma, Rome, Italy

http://w3.uniroma1.it/monaco

[email protected]

M16

08/04/2013 12/04/2013About Nonlinear Digital Control


To understand the effect of sampling over the control properties of a continuous-timepyisical process is preliminar to the design of a control law implemented through digital

devices. Starting from this analysis equivalent and approximated sampled-data

representations will be introduced. On the bases of new concepts and definitions in

discrete-time, sampled-data control schemes are proposed to solve well known nonlinear

control problems with reference to different classes of processes. Some case studies

illustrate the computational aspects and the performances of the sampled-data control

systems.

Topics include:

Nonlinear sampling and the properties of the-sampled data model

Feedback linearization and tracking

Passivity based control

Lyapunov design and back-stepping techniques

Delayed systems

Some case studies


18/27


M17

22/04/2013 26/04/2013Event-triggered and Self-triggered Control

Maurice HeemelsHybrid and Networked Systems group

Department of Mechanical Engineering

Technische Universiteit Eindhoven (TU/e)

Netherlands

http://www.dct.tue.nl/heemels

[email protected]

Karl H. JohanssonACCESS Linnaeus Centre

School of Electrical Engineering

KTH Royal Institute of Technology

Sweden

http://www.ee.kth.se/~kallej

[email protected]


Classical sampled-data control is based on periodic sensing and actuation. Due to recent

developments in computer and communication technologies, a new type of resource-

constrained wireless embedded control systems is emerging. It is desirable in these systems to

limit the sensor and control communication to instances when the system needs attention. This

requirement calls for a paradigm shift in digital control implementations towards event-triggered

and self-triggered control systems. Event-triggered control is reactive and generates sensor

sampling and control actuation when, for instance, the plant state deviates more than a certain

threshold from a desired value. Self-triggered control, on the other hand, is proactive and

computes the next sampling or actuation instance ahead of time. As in both schemes the

sampling period is varying, the vast literature on sampled-data control is no longer applicable to

guarantee desirable closed-loop stability and performance properties. As a consequence, a new

system theory for event-triggered and self-triggered control is needed. This course will provide

an introduction to event-triggered and self-triggered control systems.

Topics:

The basics of event-triggered and self-triggered control will be presented showing the status and

open problems in the emerging system theory for these new digital control strategies. Different

design perspectives will be provided for both state feedback and output feedback event-

triggered control and various types of event-triggering mechanisms. Also distributed variants,

which are suitable for large-scale control applications, will be discussed in detail. The

implementation of event- and self-triggered control using existing wireless communicationtechnology and interesting applications to wireless control in the process industry will also be

presented.

Paulo TabuadaCyber-Physical Systems Laboratory

Department of Electrical Engineering

University of California, Los Angeles

USA

http://www.ee.ucla.edu/~tabuada

http://www.cyphylab.ee.ucla.edu

[email protected]


19/27


M18 - ISTANBUL

22/04/2013 26/04/2013

Stochastic Control with Contemporary Methods

and Applications


In many applications, stochastic models are being turned

to as the most effective description of control problems.

This is especially true in the study of highly autonomous

systems, where learning may be involved, and also in

financial engineering when stochastic models have long

been seen as essential. Often the combination of Markovmodels and ordinary differential equations provide

natural and effective descriptions. However, teaching

stochastic processes to students whose primary interests

are in applications has long been a problem. On one

hand, the subject can quickly become highly technical

and if mathematical concerns are allowed to dominate

there may be no time available for exploring the many

interesting areas of applications. On the other hand, the

treatment of stochastic calculus in a cavalier fashionleaves the student with a feeling of great uncertainty

when it comes to exploring new material. This problem

has become more acute as the power of the differential

equation point of view has become more widely

appreciated.

In this course we will resolve this dilemma with the needs of those interested in building models

and designing algorithms for learning, estimation and control in mind. The approach is to start

with Poisson counters and to identify the Wiener process with a certain limiting form. ThePoisson counter and differential equations whose right-hand sides include the differential of

Poisson counters are developed first. This leads to the construction of a sample path (Ito)

representations of a continuous time jump process using Poisson counters. This point of view

leads to an efficient problem solving technique and permits a unified treatment of time varying

and nonlinear problems. More importantly, it provides sound intuition for stochastic differential

equations and their uses without allowing the technicalities to dominate. A variety of models

will be developed. For example, the wide spread interest in problems arising in speech

recognition and computer vision has influenced the choice of topics in several places. Examples

will be drawn from applied work in communications (wireless), artificial intelligence (pathplanning), physics (NMR), and other branches of mathematics.

Roger W. Brockett

Harvard School of Engineeringand Applied Sciences, USA

http://www.seas.harvard.edu/directory/brockett

[email protected]


20/27


M19 - ISTANBUL

29/04/2013 03/05/2013Symbolic Control Design of Cyber-Physical Systems

Maria Domenica Di BenedettoDipartimento di Ingegneria e

Scienze dell'informazione e Matematica

Center of Excellence DEWS

University of LAquila, Italy

http://www.diel.univaq.it/people/dibenedetto/

Alessandro BorriIstituto di Analisi dei Sistemi ed

Informatica "A. Ruberti" (IASI)

Consiglio Nazionale delle Ricerche (CNR)

Rome, Italyhttp://www.alessandroborri.it/


Cyber-Physical Systems (CPS) are large-scale, complex, heterogeneous, distributed and

networked systems where physical processes interact with distributed computing units

through communication networks. Formal approaches to the control design of these

systems are relatively unexplored today. This course will present an approach to the control

design of CPS based on symbolic models. Symbolic models are finite state automata where

each state corresponds to an aggregate of possibly infinite continuous states and each label

on the transitions to an aggregate of possibly infinite continuous inputs. We will show how

the use of symbolic models provides a systematic approach to deal with control problems

where software and hardware interact with the physical world through non-ideal

communication networks. Efficient on-the-fly algorithms for symbolic control design will

also be discussed. We will illustrate the proposed methodology on case studies.

The following topics will be covered in the course:

* Transition systems, equivalence and compositionality

* Approximation metrics for discrete and continuous systems

* Incremental stability notions for nonlinear systems

* Symbolic models for nonlinear and networked control systems

* Symbolic control design* Efficient on-the-fly algorithms and case studies

Giordano PolaDipartimento di Ingegneria e

Scienze dell'informazione e Matematica

Center of Excellence DEWS

University of LAquila, Italyhttp://www.diel.univaq.it/people/pola/


21/27


Romeo Ortega

Laboratoire des Signaux et SystmesCNRS-Univ.ParisSud-Supelec, Gif-sur-Yvette, France

https://www.l2s.supelec.fr/perso/ortega

[email protected]

Alessandro Astolfi

Department of Electrical and Electronic EngineeringImperial College, London, UK

http://www3.imperial.ac.uk/people/a.astolfi

[email protected]


Goal of this course is to present a class of recently developed control tools for the robust

stabilization, by state and output feedback, of classes of nonlinear systems. These new tools

enable to give an alternative formulation and solution to the stabilization problem for general

nonlinear systems by means of the notions ofsystems immersion and manifold invariance (I&I).

I&I methods are particularly suited to robustify, with respect to unmodelled dynamics, a givencontroller scheme. They have also proved useful in adaptive control problems, where a stabilizing

controller parameterized in terms of some unknown constant vector is assumed to be known.

Adaptive control applications will be the main focus of this workshop. The proposed I&I

approach, which is partly reminiscent of early contributions in the area of PI adaptation, is shown

to yield superior performance, when compared with classical methods, and to provide improved

design flexibility and additional tuning parameters. Moreover, this approach does not require

linear parameterization, it can naturally include sign constraints in the estimated parameters,

and yields a new class of non-certainty equivalent control laws. From a Lyapunov perspective this

is the first systematic method to construct non-separable Lyapunov functions, i.e. Lyapunov

functions containing cross terms depending upon the system state and the parameters

estimation error, without assuming a specific structure of the nonlinear system to be controlled.

The theory is illustrated by means of applications and experimental results. In particular,

solutions to the adaptive stabilization problem for classes of power converters and electrical

machines and for the problem of visual servoing of a planar robot are discussed.

Topics include: - State feedback stabilization and adaptive control via immersion and invariance

- Output feedback adaptive control via immersion and invariance

- Applications in adaptive control

- Applications to electromechanical systems

- Open problems

M20

13/05/2013 17/05/2013Nonlinear and Adaptive Control


22/27


M21

13/05/2013 17/05/2013Distributed Control


Over the past decade there has been growing in

interest in distributed control problems of

alltypes. Among these are consensus and flocking

problems, the multi-agent rendezvous problem,

distributed averaging and the distributed controlof multi-agent formations. The aim of these

lectures is to explain what these problems are

and to discuss their solutions. Related concepts

from spectral graph theory, rigid graph theory,

nonhomogeneous Markov chain theory, stability

theory,and linear system theory will be covered.

Topics include:

1. Flocking and consensus

2. Distributed averaging via broadcasting

3. Gossiping and double linear iterations

4. Multi-agent rendezvous

5. Control of formations

6. Contraction coefficients7. Convergence rates

8. Asynchronous behavior

9. Stochastic matrices, graph composition, rigid graphs

A. Stephen MorseDepartment of Electrical Engineering

Yale University, USA

http://www.eng.yale.edu/controls/

[email protected]


23/27


M22

20/05/2013 24/05/2013

Extremum Seeking Control:

Analysis and Design


A great majority of control engineering design

methods deals with the analysis and design of

transient behaviour in closed-loop systems. However,

for many engineered systems, a crucial aspect of

their operation is that their steady-state behaviour is

best in some sense. Extremum seeking techniques

provide a systematic methodology for optimization of

the steady-state behaviour via closed-looptechniques in cases when the model of the plant

and/or the cost to optimize are not known to the

designer. This on-line optimization methodology has

been successfully used in a range of engineering

applications but only recently we have developed

appropriate techniques and tools to systematically

design and analyze large classes of such systems. This

subject presents state-of-the-art methods and

techniques for extremum seeking control. We willmake direct connections to off-line continuous and

discrete nonlinear programming, adaptive control

and present detailed stability analysis, as well as

controller tuning guidelines that are invaluable to

practicing engineers.

Dragan NesicDepartment of Electrical and Electronic Eng.

The University of Melbourne,

Australia

http://people.eng.unimelb.edu.au/dnesic/

[email protected]

Topics:

Singular perturbations

Averaging

Lyapunov stability of continous-time and discrete-time nonlinear systems

Continous-time and discrete-time off-line optimization (nonlinear programming) with

examples (e.g. gradient methods, Newton schemes, etc)

Continuous-time extremum seeking (black box and gray box approaches)

Convergence analysis and tuning guidelines of continuous schemes

Discrete-time extremum seeking

Convergence analysis and tuning guidelines of discrete-time schemes


24/27



Hybrid control systems arise when controlling nonlinear

systems with hybrid control algorithms algorithms

that involve logic variables, timers, computer program,

and in general, states experiencing jumps at certainevents and also when controlling systems that are

themselves hybrid. Recent technological advances

allowing for and utilizing the interplay between digital

systems with the analog world (e.g., embedded

computers, sensor networks, etc.) have increased the

demand for a theory applicable to the resulting systems,

which are of hybrid nature, and for design techniques

that may guarantee, through hybrid control,

performance, safety, and recovery specifications even inthe presence of uncertainty. In the workshop, we will

present recent advances in the theory and design of

hybrid control systems, with focus on robustness

properties.

Ricardo G. SanfeliceDept. Aerospace

& Mechanical Engineering

University of Arizona, USA

http://www.u.arizona.edu/~sricardo/

In this course, we will present a general modeling framework for hybrid systems and

relevant modern mathematical tools. Next, we will introduce asymptotic stability and its

robustness, and describe systematic tools like Lyapunov functions and invariance

principles. The power of hybrid control for (robust) stabilization of general nonlinear

systems will be displayed in applications including control of robotic manipulators,

autonomous vehicles, and juggling systems

Topics:

M23

20/05/2013 24/05/2013

Robust Hybrid Control Systems


25/27



This course presents some modern tools for

treating truly nonlinear control problems,

including non smooth calculus and discontinuous

feedback. The need for such tools will be

motivated, and applications will be made to

central issues in optimal and stabilizing control.

The context throughout is that of systems of

ordinary differential equations, and the level will

be that of a graduate course intended for ageneral control audience.

M24LAQUILA

20/05/2013 24/05/2013

Optimality, Stabilization, and Feedback

in Nonlinear Control

Francis ClarkeInstitut Camille Jordan

Universit Claude Bernard Lyon 1, Francehttp://math.univ-lyon1.fr/~clarke/

[email protected]

Topics include:

1. Dynamic optimization: from the calculus of variations to the Pontryagin

Maximum Principle2. Some constructs of nonsmooth analysis, and why we need them

3. Lyapunov functions, classical to modern

4. Discontinuous feedback for stabilization

5. Sliding modes and hybrid systems


26/27


M25- LAQUILA

27/05/2013 31/05/2013Modeling and Estimation for control


The objective of this class is to introduce multi-

physics models for complex dynamical systems,

with different modeling, identification and

estimation methods. The purpose of such models is

to include physical knowledge of the systems as

well as experimental data, and to allow forpreliminary system design, predictive diagnostic

and real-time control.

Topics :

1. Introduction to modeling

Physical modeling2. Principles of physical modeling

3. Some Basic Relationships in Physics.

4. Bond Graphs

Simulation

5. Computer-Aided Modeling

6. Modeling and Simulation in Scilab

System identification

7. Experiment Design for System Identification

8. Non-parametric Identification9. Parameter Estimation in Linear Models

10. System Identification Principles and Model Validation

11. Nonlinear Black-box Identification

Towards process supervision

12. Recursive Estimation Methods

For more details, see

http://physique-eea.ujf-grenoble.fr/intra/Formations/M2/EEATS/PSPI/UEs/courses_MME.php

Emmanuel WitrantDpartement Automatique

CNRS Gipsa-Lab, Grenoble, France

ttp://www.gipsa-lab.grenoble-inp.fr/~e.witrant/

[email protected]


27/27


M26

27/05/2013 31/05/2013Switched Systems and Control


Switched systems are dynamical systems described

by a family of continuous-time systems and a rule

that orchestrates the switching between them. Such

systems are interesting objects for theoretical study

and provide realistic models suitable for many

applications.

This course will examine switched systems from a

control-theoretic perspective. The main focus will be

on stability analysis and control synthesis of systems

that combine continuous dynamics with switching

events. In the analysis part of the course, we will

develop stability theory for switched systems;

properties beyond traditional stability, such as

invertibility and input-to-state stability, will also bediscussed. In the synthesis part, we will investigate

several important classes of control problems for

which the logic-based switching paradigm emerges

as a natural solution.

Topics include:

Single and multiple Lyapunov functions

Stability criteria based on commutation relations

Stability under slow switching

Switched systems with inputs and outputs

Control of nonholonomic systems

Quantized feedback control

Switching adaptive control

Daniel LiberzonCoordinated Science Laboratory

University of Illinois at

Urbana-Champaign, USA

http://netfiles.uiuc.edu/liberzon/www

[email protected]

Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries

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