# Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Elektrotechnik und Informationstechnologie Master | ||||||

Fächer der Vertiefung Insgesamt 42 KP müssen im Masterstudium aus Vertiefungsfächern erreicht werden. Der individuelle Studienplan unterliegt der Zustimmung eines Tutors. | ||||||

Systems and Control | ||||||

Empfohlene Fächer Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor. | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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227-0529-00L | Liberalized Electric Power Systems and Smart Grids | W | 6 KP | 4G | R. Bacher | |

Kurzbeschreibung | This 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. | |||||

Lernziel | - 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. | |||||

Inhalt | - 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. | |||||

Skript | Class material is continuously updated and distributed to students. | |||||

Voraussetzungen / Besonderes | Requirements: Programming in any language, Numerical analysis, basics for power system models, optimization and economics, active participation (discussions) Mode of exam: examination may be computer-based | |||||

227-0530-00L | Optimization in Energy Systems | W | 6 KP | 4G | G. Hug, H. Abgottspon, M. Densing | |

Kurzbeschreibung | The course covers various aspects of optimization with a focus on applications to energy networks and scheduling of hydro power. Throughout the course, concepts from optimization theory are introduced followed by practical applications of the discussed approaches. | |||||

Lernziel | After this class, the students should have a good handle on how to approach a research question which involves optimization and implement and solve the resulting optimization problem by choosing appropriate tools. | |||||

Inhalt | In our everyday’s life, we always try to take the decision which results in the best outcome. But how do we know what the best outcome will be? What are the actions leading to this optimal outcome? What are the constraints? These questions also have to be answered when controlling a system such as energy systems. Optimization theory provides the opportunity to find the answers by using mathematical formulation and solution of an optimization problem. The course covers various aspects of optimization with a focus on applications to energy networks. Throughout the course, concepts from optimization theory are introduced followed by practical applications of the discussed approaches. The applications are focused on 1) the Optimal Power Flow problem which is formulated and solved to find optimal device settings in the electric power grid and 2) the scheduling problem of hydro power plants which in many countries, including Switzerland, dominate the electric power generation. On the theoretical side, the formulation and solving of unconstrained and constrained optimization problems, multi-time step optimization, stochastic optimization including probabilistic constraints and decomposed optimization (Lagrangian and Benders decomposition) are discussed. | |||||

227-0694-00L | Game Theory and Control | W | 4 KP | 2V + 2U | S. Bolognani, A. R. Hota, M. Kamgarpour | |

Kurzbeschreibung | Game Theory is the study of strategic decision making, and was used to solve problems in economics by John Nash (A Beautiful Mind) and others. We study concepts and methods in Game Theory, and show how these can be used to solve control design problems. The course covers non-cooperative dynamic games and Nash equilibria, and emphasizes their use in control applications. | |||||

Lernziel | Formulate an optimal control problem as a noncooperative dynamic game, compute mixed and behavioural strategies for different equilibria. | |||||

Inhalt | Introduction to game theory, mathematical tools including convex optimisation and dynamic programming, zero sum games in matrix and extensive form, pure and mixed strategies, minimax theorem, nonzero sum games in normal and extensive form, numerical computation of mixed equilibrium strategies, Nash and Stackelberg equilibria, potential games, infinite dynamic games, differential games, behavioral strategies and informational properties for dynamic games, aggregative games, VCG mechanism. | |||||

Skript | Will be made available from SPOD or course webpage. | |||||

Literatur | Basar, T. and Olsder, G. Dynamic Noncooperative Game Theory, 2nd Edition, Society for Industrial and Applied Mathematics, 1998. Available through ETH Bibliothek directly at Link. | |||||

Voraussetzungen / Besonderes | Control Systems I (or equivalent). Necessary methods and concepts from optimization will be covered in the course. | |||||

227-0696-00L | Predictive Control of Power Electronics Systems | W | 6 KP | 2V + 2U | T. Geyer | |

Kurzbeschreibung | Bridging the gap between modern control methods and power electronics, this course focuses on predictive control methods applied to power electronics systems. This includes emerging model predictive control methods (with and without a modulator), as well as classic predictive methods, such as time-optimal control and deadbeat control. This course targets power electronics and control students. | |||||

Lernziel | - Knowledge of modern time-domain control methods applied to dc-dc and dc-ac converters and their corresponding loads. These control methods include model predictive control (MPC), deadbeat control and time-optimal control. - Understanding of optimized pulse patterns and techniques to achieve fast closed-loop control. - Ability to derive suitable mathematical models. - Knowledge of and experience in optimization techniques to solve the underlying mixed-integer and quadratic programs. - Appreciation of the advantages and disadvantages of the different control methods. | |||||

Inhalt | - Review of mathematical modelling and time-domain control methods (particularly MPC and 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, formulation of fast closed-loop controllers and methods to solve the underlying quadratic programming problem. - Indirect MPC with pulse width modulation (PWM). Formulation of the MPC problem, imposition of hard and soft constraints, techniques to solve the quadratic program in real time and application to modular multilevel converters. - Summary of recent research results and activities. - Matlab / Simulink exercises to enhance the understanding of the control concepts. | |||||

Skript | The lecture is based on the recent book "Model Predictive Control of High Power Converters and Industrial Drives" by T. Geyer. Additional notes and related literature will be distributed in the class. | |||||

Voraussetzungen / Besonderes | - Power Electronic Systems I - Control Systems I (Regelsysteme I) - Signal and System Theory II | |||||

227-0945-10L | Cell and Molecular Biology for Engineers IIThis course is part II of a two-semester course. Knowledge of part I is required. | W | 3 KP | 2G | C. Frei | |

Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Inhalt | Lectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, three journal clubs will be held, where one/two publictions will be discussed. For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded, and count as 25% for the final grade. | |||||

Skript | Scripts of all lectures will be available. | |||||

Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||

151-0641-00L | Introduction to Robotics and Mechatronics Number of participants limited to 60. Enrollment is only valid through registration on the MSRL Website (Link). Registration per e-mail is no longer accepted! | W | 4 KP | 2V + 2U | B. Nelson, N. Shamsudhin | |

Kurzbeschreibung | The 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. | |||||

Lernziel | The 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. | |||||

Inhalt | An 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. | |||||

Voraussetzungen / Besonderes | The 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. | |||||

151-0854-00L | Autonomous Mobile Robots | W | 5 KP | 4G | R. Siegwart, M. Chli, J. Nieto | |

Kurzbeschreibung | The 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. | |||||

Lernziel | The 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. | |||||

Skript | This 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. | |||||

Literatur | This 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 | |||||

252-0526-00L | Statistical Learning Theory | W | 6 KP | 2V + 3P | J. M. Buhmann | |

Kurzbeschreibung | The course covers advanced methods of statistical learning : 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. | |||||

Lernziel | The 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. | |||||

Inhalt | # 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. # 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. # Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models | |||||

Skript | A draft of a script will be provided; transparencies of the lectures will be made available. | |||||

Literatur | 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 | |||||

Voraussetzungen / Besonderes | Requirements: knowledge of the Machine Learning course 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. | |||||

376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 4 KP | 2V + 1U | R. Riener, J. Duarte Barriga | |

Kurzbeschreibung | “Rehabilitation engineering” is the application of science and technology to ameliorate the handicaps of individuals with disabilities in order to reintegrate them into society. The goal of this lecture is to present classical and new rehabilitation engineering principles and examples applied to compensate or enhance especially motor deficits. | |||||

Lernziel | Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor disabilities. | |||||

Inhalt | “Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive (also communicational) disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. Orthoses support existing but affected body functions (e.g., glasses, crutches), while prostheses compensate for lost body functions (e.g., cochlea implant, artificial limbs). In case of sensory disorders, the lost function can also be substituted by other modalities (e.g. tactile Braille display for vision impaired persons). The goal of this lecture is to present classical and new technical principles as well as specific examples applied to compensate or enhance mainly motor deficits. Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited by displaying the patient with a maximum amount of information in order to compensate his/her impairment. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. Multi-modality and interactivity have the potential to increase the therapeutical outcome compared to classical rehabilitation strategies. In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics. | |||||

Skript | Lecture notes will be distributed at the beginning of the lecture (1st session) | |||||

Literatur | Introductory Books Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press. Advances in Rehabilitation Robotics – Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004. Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001. Control of Movement for the Physically Disabled. Eds.: D. Popovic, T. Sinkjaer. Springer Verlag London, 2000. Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005. Biomechanics and Neural Control of Posture and Movement. Eds.: J.M. Winters, P.E. Crago. Springer New York, 2000. Selected Journal Articles Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195. Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432 Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700. Colombo, G., Jörg, M., Jezernik, S. (2002) Automatisiertes Lokomotionstraining auf dem Laufband. Automatisierungstechnik at, vol. 50, pp. 287-295. Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206. Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87 Leifer, L. (1981): Rehabilitive robotics, Robot Age, pp. 4-11 Platz, T. (2003): Evidenzbasierte Armrehabilitation: Eine systematische Literaturübersicht, Nervenarzt, 74, pp. 841-849 Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Riener, R., Fuhr, T., Schneider, J. (2002) On the complexity of biomechanical models used for neuroprosthesis development. International Journal of Mechanics in Medicine and Biology 2, pp. 389-404. Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894. | |||||

Voraussetzungen / Besonderes | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK - Biomedical Engineering - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome |

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