Search result: Catalogue data in Spring Semester 2021
Computational Science and Engineering Master | ||||||
Fields of Specialization | ||||||
Systems and Control | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0216-00L | Control Systems II | W | 6 credits | 4G | R. Smith | |
Abstract | Introduction to basic and advanced concepts of modern feedback control. | |||||
Objective | Introduction to basic and advanced concepts of modern feedback control. | |||||
Content | This 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 notes | The slides of the lecture are available to download. | |||||
Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||
Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||
227-0224-00L | Stochastic Systems Does not take place this semester. | W | 4 credits | 2V + 1U | to be announced | |
Abstract | Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering. | |||||
Objective | Stochastic 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 notes | H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts | |||||
227-0207-00L | Nonlinear Systems and Control Prerequisite: Control Systems (227-0103-00L) | W | 6 credits | 4G | E. Gallestey Alvarez, P. F. Al Hokayem | |
Abstract | Introduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems. | |||||
Objective | On completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics. Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods. | |||||
Content | Virtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives. During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course 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 and mathematical analysis. | |||||
Lecture notes | An english manuscript will be made available on the course homepage during the course. | |||||
Literature | H.K. Khalil: Nonlinear Systems, Prentice Hall, 2001. | |||||
Prerequisites / Notice | Prerequisites: Linear Control Systems, or equivalent. | |||||
401-5850-00L | Seminar in Systems and Control for CSE | W | 4 credits | 2S | J. Lygeros | |
Abstract | Course based on individual study. Short projects involving literature review, possibly simple research tasks. | |||||
Objective | Introduce students to state of the art research in systems and control. | |||||
227-0690-12L | Advanced Topics in Control (Spring 2021) New topics are introduced every year. | W | 4 credits | 2V + 2U | F. Dörfler, M. Hudoba de Badyn, W. Mei | |
Abstract | Advanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor. During the spring of 2020, the course will cover a range of topics in distributed systems control. | |||||
Objective | By the end of this course you will have developed a sound and versatile toolkit to tackle a range of problems in network systems and distributed systems control. In particular, we will develop the methodological foundations of algebraic graph theory, consensus algorithms, and multi-agent systems. Building on top of these foundations we cover a range of problems in epidemic spreading over networks, swarm robotics, sensor networks, opinion dynamics, distributed optimization, and electrical network theory. | |||||
Content | Distributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are electric power grids, swarm robotics, sensor networks, and epidemic spreading over networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, computing, and control capabilities. This course covers algebraic graph theory, consensus algorithms, stability of network systems, distributed optimization, and applications in various domains. | |||||
Lecture notes | A complete set of lecture notes and slides will be provided. | |||||
Literature | The course will be largely based on the following set of lecture notes co-authored by one of the instructors: Link | |||||
Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra and dynamical systems. |
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