Manfred Morari: Catalogue data in Spring Semester 2016

Name Prof. em. Dr. Manfred Morari
FieldAutomatik
Address
Institut für Automatik
ETH Zürich, ETL I 29
Physikstrasse 3
8092 Zürich
SWITZERLAND
Telephone+41 44 632 76 26
Fax+41 44 632 12 11
E-mailmorari@control.ee.ethz.ch
URLhttp://control.ethz.ch
DepartmentInformation Technology and Electrical Engineering
RelationshipProfessor emeritus

NumberTitleECTSHoursLecturers
227-0103-AALControl Systems Information
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
6 credits8RM. Morari, F. Dörfler
AbstractStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
ObjectiveStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
ContentProcess automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.
Lecture notesA copy of the lecture slides can be obtained from Student Print on Demand (SPOD) for CHF 11. www.spod.ethz.ch

Exercise material is available for download at the Control Systems webpage www.control.ee.ethz.ch/~rs or in the exercise sessions.
LiteratureG.F. Franklin, J.D. Powell, A. Emami-Naeini. Feedback Control of Dynamic Systems. 6th edition, Prentice Hall, Version 2009, Reading, ISBN 978-0-1350-150-9.Softcover student's edition ca. CHF 150.-. (Spring 2010)
Prerequisites / NoticePrerequisites:
Signal and Systems Theory / MATLAB skills
227-0221-00LModel Predictive Control Information Restricted registration - show details
Enrolling necessary (see "Notice").
6 credits4GM. Morari, M. Zeilinger
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: baumasab@control.ee.ethz.ch) is required. Please give information on your "Studienrichtung", semester, institute, etc.
After your reservation has been confirmed, please register online at www.mystudies.ethz.ch.

Interested persons from outside ETH:
It is not possible/needed to enrol as external auditor for this course. Please contact Sabrina Baumann to register for the course (baumasab@control.ee.ethz.ch).

We have only a limited number of places in the course, it is "first come, first served"!