Search result: Catalogue data in Spring Semester 2015

Electrical Engineering and Information Technology Master Information
Major Courses
A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval.
Systems and Control
Core Subjects
These core subjects are particularly recommended for the field of "Systems and Control".
NumberTitleTypeECTSHoursLecturers
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
227-0207-00LNonlinear Systems and Control Information
Prerequisite: Control Systems (227-0103-00L)
W6 credits4GE. Gallestey Alvarez, P. F. Al Hokayem
AbstractTo introduce students to the area of nonlinear systems and control, to familiarize them with tools for modelling and analysing nonlinear systems and to provide an overview of the various nonlinear controller design methods.
ObjectiveOn completion of the course, students understand the difference between linear and nonlinear systems, know the the mathematical techniques for modeling and analysing these systems, and have learnt various methods for designing controllers for these systems.
Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of virtues and drawbacks in the different methods.
ContentVirtually all practical control problems are of nonlinear nature. In some
cases the application of linear control methods will lead to satisfying controller performance. In many other cases only application of nonlinear analysis and synthesis methods will guarantee achievement of the desired objectives. During the past decades a number of practically applicable and mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for modelling and analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons, and the students will be familiarized with the basic concepts of nonlinear control theory.

This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control.
Lecture notesAn english manuscript will be made available on the course homepage during the course.
LiteratureH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Prerequisites / NoticePrerequisites: Linear Control Systems, or equivalent.
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
Lecture notesThe slides of the lecture are available to download
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0221-00LModel Predictive Control Information Restricted registration - show details
Enrolling necessary (see "Notice").
W6 credits4GM. Morari
AbstractSystem complexity and demanding performance render traditional control inadequate. Applications from the process industry to the communications sector increasingly use MPC. The last years saw tremendous progress in this interdisciplinary area. The course first gives an overview of basic concepts and then uses them to derive MPC algorithms. There are exercises and invited speakers from industry.
ObjectiveIncreased system complexity and more demanding performance requirements have rendered traditional control laws inadequate regardless if simple PID loops are considered or robust feedback controllers designed according to some H2/infinity criterion. Applications ranging from the process industries to the automotive and the communications sector are making increased use of Model Predictive Control (MPC), where a fixed control law is replaced by on-line optimization performed over a receding horizon. The advantage is that MPC can deal with almost any time-varying process and specifications, limited only by the availability of real-time computer power.
In the last few years we have seen tremendous progress in this interdisciplinary area where fundamentals of systems theory, computation and optimization interact. For example, methods have emerged to handle hybrid systems, i.e. systems comprising both continuous and discrete components. Also, it is now possible to perform most of the computations off-line thus reducing the control law to a simple look-up table.
The first part of the course is an overview of basic concepts of system theory and optimization, including hybrid systems and multi-parametric programming. In the second part we show how these concepts are utilized to derive MPC algorithms and to establish their properties. On the last day, speakers from various industries talk about a wide range of applications where MPC was used with great benefit.
There will be exercise sessions throughout the course where the students can test their understanding of the material. We will make use of the MPC Toolbox for Matlab that is distributed by MathWorks.
ContentTentative Program

Day 1: Linear Systems I
Fundamentals of linear system theory – Review (system representations, poles, zeros, stability, controllability & observability, stochastic system descriptions, modeling of noise).

Day 2: Linear Systems II
Optimal control and filtering for linear systems (linear quadratic regulator, linear observer, Kalman Filter, separation principle, Riccati Difference Equation).

Days 3 and 4: Basics on Optimization
Fundamentals of optimization (linear programming, quadratic programming, mixed integer linear/quadratic programming, duality theory, KKT conditions, constrained optimization solvers).
Exercises.

Day 5: Introduction to MPC
MPC – concept and formulation, finite horizon optimal control, receding horizon control, stability and feasibility, computation.
Exercises.

Day 6: Numerical methods for MPC
Unconstrained Optimization, Constrained Optimization, Software applications

Day 7: Practical Aspects, Explicit & Hybrid MPC
- Reference tracking and soft constraints
- Explicit solution to MPC for linear constrained systems. Motivation. Introduction to (multi)-parametric programming through a simple example. Multi-parametric linear and quadratic programming: geometric algorithm. Formulation of MPC for linear constrained systems as a multi-parametric linear/quadratic program. A brief introduction to Multi-parametric Toolbox.
- MPC for discrete-time hybrid systems. Introduction to hybrid systems. Models of hybrid systems (MLD, DHA, PWA, etc.). Equivalence between different models. Modelling using HYSDEL. MLD systems. MPC based on MILP/MIQP. Explicit solution: mpMILP. Short introduction into dynamic programming (DP). Computation of the explicit MPC for PWA systems based on DP. Exercises.

Day 8: Applications
Invited speakers from industry and academia, different case studies

Day 9
Design exercise
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticePrerequisites:
One semester course on automatic control, Matlab, linear algebra.

ETH students:
As participation is limited, a reservation (e-mail: Link) is required. Please give information on your "Studienrichtung", semester, institute, etc.
After your reservation has been confirmed, please register online at Link.

Interested persons from outside ETH:
It is not possible/needed to enrol as external auditor for this course. Please contact Alain Bolle to register for the course (Link).

We have only a limited number of places in the course, it is "first come, first served"!
227-0224-00LStochastic Systems Information W4 credits2V + 1UF. Herzog
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0690-06LAdvanced Topics in Control (Spring 2015) Information
New topics are introduced every year.
W4 credits2V + 2UF. Dörfler
AbstractThis class will introduce students to advanced, research level topics in the area of automatic control. Coverage varies from semester to semester, repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will concentrate on distributed systems and control.
ObjectiveThe intent is to introduce students to advanced research level topics in the area of automatic control. The course is jointly organized by Prof. R. D'Andrea, L. Guzzella, J. Lygeros, M. Morari, R. Smith, and F. Dörfler. Coverage and instructor varies from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will be taught by F. Dörfler and will focus on distributed systems and control.
ContentDistributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are the electric power grid, camera networks, and robotic sensor networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, and control capabilities. This course covers modeling, analysis, and design of distributed control systems.

Topics covered in the course include:
- the theory of graphs (with an emphasis on algebraic and spectral graph theory);
- basic models of multi-agent and interconnected dynamical systems;
- continuous-time and discrete-time distributed averaging algorithms (consensus);
- coordination algorithms for rendezvous, formation, flocking, and deployment;
- applications in robotic coordination, coupled oscillators, social networks, sensor networks, electric power grids, epidemics, and positive systems.
Lecture notesA set of self-contained set of lecture nodes will be made available on the course website.
LiteratureRelevant papers and books will be made available through the course website.
Prerequisites / NoticeControl systems (227-0216-00L), Linear system theory (227-0225-00L), or equivalents, as well as sufficient mathematical maturity.
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