Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Search result: Catalogue data in Spring Semester 2016

Robotics, Systems and Control Master Information
Core Courses
151-0306-00LVisualization, Simulation and Interaction - Virtual Reality I Information W4 credits4GA. Kunz
AbstractTechnology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).
ObjectiveThe product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.
ContentIntroduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.
Lecture notesA complete version of the handout is also available in English.
Prerequisites / NoticeVoraussetzungen:
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF

Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
151-0532-00LNonlinear Dynamics and Chaos I Information
Does not take place this semester.
This course will take place in the Autumn Semester from 2016 onwards.
W4 credits2V + 1UG. Haller
AbstractBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
ObjectiveThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Content(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
Lecture notesThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Prerequisites / Notice- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
151-0534-00LAdvanced DynamicsW4 credits2V + 1UP. Tiso, G. Haller
AbstractLagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response
ObjectiveThis course provides the students of mechanical engineering with fundamental analytical mechanics for the study of complex mechanical systems .We introduce the powerful techniques of principle of virtual work and virtual power to systematically write the equation of motion of arbitrary systems subjected to holonomic and non-holonomic constraints. The linearisation around equilibrium states is then presented, together with the concept of linearised stability. Linearized models allow the study of small amplitude vibrations for unforced and forced systems. For this, we introduce the concept of vibration modes and frequencies, modal superposition and modal truncation. The case of the vibration of light damped systems is discussed. The kinematics and dynamics of 3D rigid bodies is also extensively treated.
Lecture notesLecture notes are produced in class and are downloadable right after each lecture.
LiteratureThe students will prepare their own notes. A copy of the lecture notes will be available.
Prerequisites / NoticeMechanics III or equivalent; Analysis I-II, or equiivalent; Linear Algebra I-II, or equivalent.
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:
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
151-0607-00LOptimal & Learning Control for Autonomous Robots Information
Does not take place this semester.
W4 credits3GJ. Buchli
AbstractThe students will learn the fundamentals of optimal and learning control. They will learn how these fundamental ideas can be applied to real world problems encountered in autonomous and articulated robots.
ObjectiveAfter this lecture the students will have the understanding and tools to apply learning and optimal control to problems encountered in robotics and other fields.
Lecture notesA basic script and slide handouts will be given.
LiteratureStengel, Optimal Control and Estimation

Bertsekas, Dynamic Programming & Optimal Control

Sutton & Barto, Reinforcement Learning: An Introduction

Additional literature will be given in the class.
Prerequisites / NoticeGood knowledge of Linear Algebra, Multivariable Calculus, Probability, and Basic Control Theory is a must. Good programming skills and knowledge of PDEs and ODEs are highly recommended. Classes on optimal control, as well as on probability are a recommended preparation for this class.
151-0630-00LNanorobotics Information W4 credits2V + 1US. Pané Vidal, B. Nelson
AbstractNanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field.
ObjectiveThe aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine. Throughout the course, discussions and lab tours will be organized on selected topics.
151-0641-00LIntroduction to Robotics and Mechatronics Information Restricted registration - show details
Number of participants limited to 60.

Enrollment is only valid through registration on the MSRL Website ( and will open on 16 December 2015. Registration per e-mail is no longer accepted!
W4 credits2V + 2UB. Nelson
AbstractThe aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use.
ObjectiveThe aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.
ContentAn ever increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.
Prerequisites / NoticeThe registration is limited to 60 students.
There are 4 credit points for this lecture.
The lecture will be held in English.
The students are expected to be familiar with C programming.
151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, M. Rufli
AbstractThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
ObjectiveThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notesThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteratureThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
151-1115-00LAircraft Aerodynamics and Flight MechanicsW4 credits3GJ. Wildi
AbstractEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight test. Wind tunnel test.
Objective- Knowledge of methods to solve flight mechanic problems
- To be able to apply basic methods for flight performence calculation and stability investigations
- Basic knowledge of flight and wind tunnel tests and test evaluation methods
ContentEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight testing. Wind tunnel testing.
Lecture notesAusgewählte Kapitel der Flugtechnik (J. Wildi)
LiteratureMc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics (John Wiley and Sons), 1979 / 1995

Anderson, J: Fundamentals of Aerodynamics (McGraw-Hill Comp Inc), 2010
Prerequisites / NoticeVoraussetzungen: Grundlagen der Flugtechnik
227-0124-00LEmbedded Systems Information W6 credits4GL. Thiele
AbstractComputer systems for controlling industrial devices are called embedded systems (ES). Specifically the following topics will be covered: Design methodology, software design, real-time scheduling and operating systems, architectures, distributed embedded systems, low-power and low-energy design, architecture synthesis.
ObjectiveIntroduction to industrial applications of computer systems; understanding specific requirements and problems arising in such applications. The focus of this lecture is on the implementation of embedded systems using formal methods and computer-based synthesis methods.
ContentComputer systems for controlling industrial devices are called embedded systems (ES). ES not only have to react to random events in their environment in a timely manner, they also have to calculate control values from continuous sequences of measurements. Embedded computer systems are connected to their environment though sensors and actors. The great interest in the systematic design of heterogeneous reactive systems is caused by the growing diversity and complexity of applications for ES, the requirement for low development and testing costs, and by progress in key technologies. Specifically the following topics will be covered: Design methodology, software design, real-time scheduling and operating systems, architectures, distributed embedded systems, low-power and low-energy design, architecture synthesis. See: .
Lecture notesMaterial/script, publications, exercise sheets, podcast. See: .
Literature[Mar07] P. Marwedel. Eingebettete Systeme. Springer Verlag, Paperback, December 2007. ISBN 978-3-540-34048-5

[Mar11] P. Marwedel. Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems. Springer Verlag, Paperback, 2011. ISBN 978-94-007-0256-1

[Tei07] J. Teich. Digitale Hardware/Software-Systeme: Synthese und Optimierung. Springer Verlag, 2007. ISBN 3540468226

[But11] G.C. Buttazzo. Hard real-time computing systems: predictable scheduling algorithms and applications. Springer Verlag, Berlin, 2011. ISBN-10: 1461406757, ISBN-13: 9781461406754

[Wolf12] W. Wolf. Computers as components: principles of embedded computing system design. Morgan Kaufmann, 2012. ISBN-10: 0123884365, ISBN-13: 978-0123884367
Prerequisites / NoticePrerequisites:
Basic course in computer engineering; knowledge about distributed systems and concepts for their description.
227-0207-00LNonlinear Systems and Control Information
Prerequisite: Control Systems (227-0103-00L)
W6 credits4GE. Gallestey Alvarez, P. F. Al Hokayem
AbstractIntroduce students to the area of nonlinear systems and their control. Familiarize them with tools for modelling and analysis of nonlinear systems. 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 however, only application of nonlinear analysis and synthesis methods will guarantee achievement of the desired objectives. During the past decades a number of 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, 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).

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

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: is required. Please give information on your "Studienrichtung", semester, institute, etc.
After your reservation has been confirmed, please register online at

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 (

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
227-0248-00LPower Electronic Systems II Information W6 credits4GJ. W. Kolar
AbstractThis course details structures, operating ranges, and control concepts of modern power electronic systems to provide a deeper understanding of power electronic circuits and power components. Most recent concepts of high switching frequency AC/DC converters and AC/AC matrix inverters are presented. Simulation exercises, implemented in GeckoCIRCUITS, are used to consolidate the concepts discussed.
ObjectiveThe objective of this course is to convey knowledge of structures, operating ranges, and control concepts of modern power electronic systems. Further objectives are: to know most recent concepts and operation modes of high switching frequency AC/DC converters and AC/AC matrix inverters; to develop a deeper understanding of multi-pulse power converter circuits, transformers, and electromechanical energy converters; and to understand in-depth details of power electronic systems. Simulation exercises, implemented in the electric circuit simulator GeckoCIRCUITS, are used to consolidate the presented theoretical concepts.
ContentConverter dynamics and control: State Space Averaging, transfer functions, controller design, impact of the input filter on the converter transfer functions.
Performance data of single-phase and three-phase systems: effect of different loss components on the efficiency characteristics, linear and non-linear single phase loads, power flow of general three-phase systems, space vector calculus.
Modeling and control of three-phase PWM rectifiers: system characterization using rotating coordinates, control structure, transfer functions, operation with symmetrical and unsymmetrical mains voltages.
Scaling laws of transformers and electromechanical actuators.
Drives with permanent magnet synchronous machines: basic function, modeling, field-oriented control.
Unidirectional AC/DC converters and AC/AC converters: voltage and current DC link converters, indirect and direct matrix converters.
Lecture notesLecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.
Prerequisites / NoticePrerequisites: Introductory course on power electronics.
227-0528-00LPower System Dynamics, Control and Operation Information W6 credits4GG. Hug, A. Ulbig, M. Zima
AbstractDynamic processes in power systems, load-frequency control, voltage control, stability, line protection.
ObjectiveDynamic processes in power systems, load-frequency control, voltage control, stability, line protection.
ContentDynamical properties of electric machines, networks, loads and integrated systems. Models of power plants, turbines, turbine control, load-frequency control, tie-line control. Models of synchronous machines. Equal area criterion. Small signal stability. Voltage control and static stability. Properties of protection systems: dependability, reliability, selectivity, back-up functions, economy. Line protections: Influence of fault impedance, grounding, time setting. Differential protections. Digital protections. Intelligent protections.
Lecture notesLecture notes. WWW pages.
227-0529-00LLiberalized Electric Power Systems and Smart Grids Information W6 credits4GR. Bacher
AbstractThis class begins by discussing the paths from monopolies towards liberalized electric power markets with the grid as natural monopoly. After going through detailed mainly transmission grid constrained market models, SmartGrids models and approaches are introduced for the future distribution grid.
Objective- Understanding the legal, physical and market based framework for transmission based electric power systems.
- Understanding the market models for a secure and market based day-ahead operation of Smart Power Systems.
- Understanding Smart Grids and their market-compatible models
- Gaining experience with the formulation, implementation and computation of constrained electricity markets for transmission and Smart distribution systems.
Content- Legal conditions for the regulation and operation of electric power systems (CH, EU).
- Modelling physical laws, objectives and constraints of electric power systems at transmission and smart distribution level.
- Optimization as mathematical tool to achieve maximum society profits and considering at the same time grid based constraints and incentives towards distributed / renewable energy ressources.
- Various electricity market models, their advantages and disadvantages.
- SmartGrids: The new energy system and compatibility issues with traditional market models and regulation.
Lecture notesText book is continuously updated and distributed to students.
LiteratureClass text book contains active hyperlinks related to background material.
Prerequisites / NoticeNumerical analysis, basics for power system models, optimization and economics, active participation (discussions)
227-0690-07LAdvanced Topics in Control (Spring 2016) 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 2016 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 2016 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 notes will be made available.
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.
227-0696-00LPredictive Control of Power Electronics Systems Information W6 credits2V + 2UT. Geyer
AbstractBridging the gap between modern control methods and power electronics, this course focuses on the most commonly used predictive control methods applied to power electronics systems. This includes emerging model predictive control methods both without and with a modulator, as well as more traditionally used predictive methods, such as time-optimal control and deadbeat control.
Objective- Knowledge of modern time-domain control methods applied to dc-dc and dc-ac converters and their corresponding loads (such as three-phase machines or the grid). These control methods include MPC, LQR, deadbeat and time-optimal control.
- Understanding of optimized pulse patterns and techniques to achieve fast closed-loop control.
- Derivation of suitable mathematical models of power electronics systems based on which controllers can be designed.
- Optimization techniques to solve the mixed-integer and quadratic programs underlying MPC.
- Matlab / Simulink exercises are used to further the understanding of the control concepts.
Content- Review of mathematical modelling and time-domain control methods (LQR, MPC, deadbeat control).
- Time-optimal control, deadbeat control and MPC of dc-dc converters.
- Direct MPC with reference tracking (finite control set MPC). Derivation of mathematical models of three-phase power electronics systems, formulation of the control problem, techniques to solve the one-step and the multi-step horizon problems using branch and bound techniques.
- MPC with optimized pulse patterns (OPPs). Computation of OPPs offline, formulation of fast closed-loop controllers and methods to solve the underlying quadratic programming problem.
- MPC with pulse width modulation (PWM). Review of deadbeat control methods. Formulation of the MPC problem, imposition of hard and soft constraints, techniques to solve the quadratic program in real time and application to modular multi-level converters.
- Summary of recent research results and activities.
Lecture notesThe lecture will be largely based on the recent book Model Predictive Control of High Power Converters and Industrial Drives by the lecturer. Additional notes and related literature will be distributed in the class.
Prerequisites / Notice- Signal and system theory II
- Power electronic systems I
- Control systems (Regelsysteme)
252-0526-00LStatistical Learning Theory Information W4 credits2V + 1UJ. M. Buhmann
AbstractThe course covers advanced methods of statistical learning :
PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
ObjectiveThe course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Content# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification.
# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.
# Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error?
# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:

* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing

# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.
# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.
# Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
Lecture notesno script; transparencies of the lectures will be made available.
LiteratureDuda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.

Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.

L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Prerequisites / NoticeRequirements:

basic knowledge of statistics, interest in statistical methods.

It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
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