Search result: Catalogue data in Autumn Semester 2018

Computational Science and Engineering Master Information
Fields of Specialization
Systems and Control
NumberTitleTypeECTSHoursLecturers
227-0103-00LControl Systems Information W6 credits2V + 2UF. 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.
LiteratureK. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
Prerequisites / NoticePrerequisites: Signal and Systems Theory II.

MATLAB is used for system analysis and simulation.
227-0225-00LLinear System TheoryW6 credits5GM. Kamgarpour
AbstractThe class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
ObjectiveStudents should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content- Proof techniques and practices.
- Linear spaces, normed linear spaces and Hilbert spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, duality. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
Lecture notesAvailable on the course Moodle platform.
Prerequisites / NoticeSufficient mathematical maturity with special focus on logic, linear algebra, analysis.
151-0575-01LSignals and Systems Information W4 credits2V + 2UA. Carron, G. Ducard
AbstractSignals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.
ObjectiveMaster the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.
ContentDiscrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.
Lecture notesLecture notes available on course website.
Prerequisites / NoticeControl Systems I is helpful but not required.
151-0563-01LDynamic Programming and Optimal Control Information W4 credits2V + 1UR. D'Andrea
AbstractIntroduction to Dynamic Programming and Optimal Control.
ObjectiveCovers the fundamental concepts of Dynamic Programming & Optimal Control.
ContentDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteratureDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Prerequisites / NoticeRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
252-0535-00LAdvanced Machine Learning Information W8 credits3V + 2U + 2AJ. M. Buhmann
AbstractMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
ObjectiveStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
ContentThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Prerequisites / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.
401-5850-00LSeminar in Systems and Control for CSEW4 credits2SF. Dörfler, R. Smith
Abstract
Objective
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