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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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EuropeanEmbeddedControl Institute
Roberto TempoCNR-IEIIT, Politecnico di Torino, Italy
http://staff.polito.it/roberto.tempo/
Fabrizio DabbeneCNR-IEIIT, Politecnico di Torino, Italy
http://staff.polito.it/fabrizio.dabbene/
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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EuropeanEmbeddedControl Institute
Marco C. CampiDepartment of Information Engineering
University of Brescia, Italy
http://www.ing.unibs.it/~campi/
Simone GarattiDipartimento di Elettronica ed Informazione
Politecnico di Milano, Italy
http://home.dei.polimi.it/sgaratti/
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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4/27
EuropeanEmbeddedControl Institute
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
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EuropeanEmbeddedControl Institute
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
Claude SamsonINRIA, France
http://www.inria.fr/personnel/Claude.Samson.fr.html
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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6/27
EuropeanEmbeddedControl Institute
Antonio LoriaCNRS - France
http://www.lss.supelec.fr/perso/loria/
Elena PanteleyCNRS - France
http://www.lss.supelec.fr/perso/panteley/
M5
11/02/2013 15/02/2013
Design and Analysis Tools for
Physical Control Systems
Abstract of the course:
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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EuropeanEmbeddedControl Institute
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/
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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EuropeanEmbeddedControl Institute
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
Marcello FarinaDipartimento di Elettronica ed Informazione
Politecnico di Milano, Italy
http://home.dei.polimi.it/farina
M7
25/02/2013 01/03/2013Decentralized and Distributed Control
Abstract of the course:
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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EuropeanEmbeddedControl Institute
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
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EuropeanEmbeddedControl Institute
M9
11/03/2013 15/03/2013Stability and Control of Time-delay Systems
Abstract of the course:
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
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/
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EuropeanEmbeddedControl Institute
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/
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EuropeanEmbeddedControl Institute
M11 - BELGRADE
12/03/2013 16/03/2013Control of Nonlinear Delay Systems and PDEs
Abstract of the course:
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]
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EuropeanEmbeddedControl Institute
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
Ufuk TopcuControl and Dynamical Systems
California Institute of Technology, USA
http://www.cds.caltech.edu/~utopcu
Abstract of the course:
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/
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EuropeanEmbeddedControl Institute
Luca ZaccarianLAAS-CNRS, Toulouse, France
http://homepages.laas.fr/lzaccari/
M13
18/03/2013 22/03/2013
Input Saturation: Control Design and
Anti-windup
Abstract of the course:
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/
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EuropeanEmbeddedControl Institute
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
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
16/27
EuropeanEmbeddedControl Institute
M15
25/03/2013 29/03/2013Model Predictive Control
Abstract of the course:
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]
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
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EuropeanEmbeddedControl Institute
Marie Dorothe Normand-CyrotLaboratoire des Signaux et Systmes
CNRS-Univ.ParisSud-Supelec, Gif-sur-Yvette, France
https://www.l2s.supelec.fr/perso/cyrot
Salvatore MonacoDipartimento di Ingegneria Informatica, Automatica
e Gestionale Antonio Ruberti
Sapienza Universit di Roma, Rome, Italy
http://w3.uniroma1.it/monaco
M16
08/04/2013 12/04/2013About Nonlinear Digital Control
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
18/27
EuropeanEmbeddedControl Institute
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
Karl H. JohanssonACCESS Linnaeus Centre
School of Electrical Engineering
KTH Royal Institute of Technology
Sweden
http://www.ee.kth.se/~kallej
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
19/27
EuropeanEmbeddedControl Institute
M18 - ISTANBUL
22/04/2013 26/04/2013
Stochastic Control with Contemporary Methods
and Applications
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
20/27
EuropeanEmbeddedControl Institute
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/
Abstract of the course:
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/
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
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EuropeanEmbeddedControl Institute
Romeo Ortega
Laboratoire des Signaux et SystmesCNRS-Univ.ParisSud-Supelec, Gif-sur-Yvette, France
https://www.l2s.supelec.fr/perso/ortega
Alessandro Astolfi
Department of Electrical and Electronic EngineeringImperial College, London, UK
http://www3.imperial.ac.uk/people/a.astolfi
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
22/27
EuropeanEmbeddedControl Institute
M21
13/05/2013 17/05/2013Distributed Control
Abstract of the course:
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/
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
23/27
EuropeanEmbeddedControl Institute
M22
20/05/2013 24/05/2013
Extremum Seeking Control:
Analysis and Design
Abstract of the course:
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/
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
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EuropeanEmbeddedControl Institute
Abstract of the course:
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
25/27
EuropeanEmbeddedControl Institute
Abstract of the course:
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/
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
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
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EuropeanEmbeddedControl Institute
M25- LAQUILA
27/05/2013 31/05/2013Modeling and Estimation for control
Abstract of the course:
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/
7/30/2019 Www.eeci Institute.eu EECI Docs2 EECI Modules 2013 Summaries
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EuropeanEmbeddedControl Institute
M26
27/05/2013 31/05/2013Switched Systems and Control
Abstract of the course:
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
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