Search result: Catalogue data in Spring Semester 2015
Robotics, Systems and Control Master | ||||||
Core Courses | ||||||
Robot Design, Modelling and Control | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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151-0641-00L | Introduction to Robotics and Mechatronics Number of participants limited to 60. COURSE IS FULLY BOOKED! The enrollment is only valid if an e-mail is sent to Link with "IRM participation" in the subject. Enrollment is valid starting from September 2014. The order of enrollment will be considered according to the time your e-mail is sent. | W | 4 credits | 2V + 2U | B. Nelson | |
Abstract | 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. | |||||
Objective | 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. | |||||
Content | 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. | |||||
Prerequisites / Notice | 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 credits | 4G | P. Furgale, M. Hutter, M. Rufli, D. Scaramuzza, R. Siegwart | |
Abstract | 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. | |||||
Objective | 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. | |||||
Lecture notes | 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. | |||||
Literature | 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 | |||||
376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 3 credits | 2V + 1U | R. Riener | |
Abstract | 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. | |||||
Objective | Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor disabilities. | |||||
Content | “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. | |||||
Lecture notes | Lecture notes will be distributed at the beginning of the lecture (1st session) | |||||
Literature | 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. | |||||
Prerequisites / Notice | 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 | |||||
151-0630-00L | Nanorobotics | W | 4 credits | 2V + 1U | S. Pané Vidal, B. Nelson | |
Abstract | Nanorobotics 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. | |||||
Objective | The 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. | |||||
151-0104-00L | Uncertainty Quantification for Engineering & Life Sciences Does not take place this semester. Number of participants limited to 40. | W | 4 credits | 3G | P. Koumoutsakos | |
Abstract | Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology. | |||||
Objective | The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures. | |||||
Content | Topics that will be covered include: Uncertainty quantification under parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis. | |||||
Lecture notes | The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class. | |||||
Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes | |||||
Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling | |||||
401-0686-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 credits | 4G | M. Troyer, P. Koumoutsakos | |
Abstract | ||||||
Objective | ||||||
Systems Engineering: Design and Optimization of Products and Systems | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 3 credits | 2V + 1U | R. Riener | |
Abstract | 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. | |||||
Objective | Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor disabilities. | |||||
Content | “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. | |||||
Lecture notes | Lecture notes will be distributed at the beginning of the lecture (1st session) | |||||
Literature | 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. | |||||
Prerequisites / Notice | 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 | |||||
227-0248-00L | Power Electronic Systems II | W | 6 credits | 4G | J. W. Kolar | |
Abstract | This 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. | |||||
Objective | The 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. | |||||
Content | Converter 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 notes | Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features. | |||||
Prerequisites / Notice | Prerequisites: Introductory course on power electronics. | |||||
227-0529-00L | SmartGrids: System Optimization of Smart and Liberalized Electric Power Systems | W | 6 credits | 4G | R. Bacher | |
Abstract | Model based optimization of SmartGrids systems considering Physics, Economics and Legislation; Optimality conditions and solutions; Lagrange-Multipliers and market prices; Price incentives in case of restrictions and grid constraints; Transmission grid congestions and implicit auctions; Security of supply with high variability + market requirements; Electricity market and SmartGrids system models. | |||||
Objective | - Understanding the legal, physical and market based framework for Smart Grid based electric power systems. - Understanding the theory of mathematical optimization models and algorithms for a secure and market based operation of Smart Power Systems. - Gaining experience with the formulation, implementation and computation of constrained optimization problems for Smart Grid and market based electricity systems. | |||||
Content | - Legal conditions for the regulation and operation of electric power systems (CH, EU). - Physical laws and constraints in electric power systems. - Special characteristics of the good "electricity". - Optimization as mathematical tool for analyzing network based electric power systems. - Types of optimization problems, optimality conditions and optimization methods. - Various electricity market models, their advantages and disadvantages. - SmartGrids: The new energy system and compatibility issues with traditional market models. | |||||
Lecture notes | Text book is continuously updated and distributed to students. | |||||
Literature | Class text book contains active hyperlinks related to back ground material. | |||||
Prerequisites / Notice | Motivation, Active participation (discussions). Numerical analysis, power system basics and modeling, optimization basics | |||||
227-0528-00L | Power System Dynamics and Control | W | 6 credits | 4G | G. Andersson, M. Zima | |
Abstract | Dynamic processes in power systems, load-frequency control, voltage control, stability, line protection. | |||||
Objective | Dynamic processes in power systems, load-frequency control, voltage control, stability, line protection. | |||||
Content | Dynamical 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 notes | Lecture notes. WWW pages. | |||||
401-0686-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 credits | 4G | M. Troyer, P. Koumoutsakos | |
Abstract | ||||||
Objective | ||||||
Physical Modelling and Simulation | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0224-00L | Stochastic Systems | W | 4 credits | 2V + 1U | F. Herzog | |
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 | |||||
151-0532-00L | Nonlinear Dynamics and Chaos I | W | 4 credits | 2V + 1U | D. Karrasch, G. Haller | |
Abstract | Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics. | |||||
Objective | This 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 notes | The 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. | |||||
151-0104-00L | Uncertainty Quantification for Engineering & Life Sciences Does not take place this semester. Number of participants limited to 40. | W | 4 credits | 3G | P. Koumoutsakos | |
Abstract | Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology. | |||||
Objective | The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures. | |||||
Content | Topics that will be covered include: Uncertainty quantification under parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis. | |||||
Lecture notes | The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class. | |||||
Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes | |||||
Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling | |||||
401-0686-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 credits | 4G | M. Troyer, P. Koumoutsakos | |
Abstract | ||||||
Objective | ||||||
151-0534-00L | Advanced Dynamics | W | 4 credits | 2V + 1U | P. Tiso, G. Haller | |
Abstract | Lagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response | |||||
Objective | This 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 notes | Lecture notes are produced in class and are downloadable right after each lecture. | |||||
Literature | The students will prepare their own notes. A copy of the lecture notes will be available. | |||||
Prerequisites / Notice | Mechanics III or equivalent; Analysis I-II, or equiivalent; Linear Algebra I-II, or equivalent. | |||||
Optimization and Control | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0104-00L | Uncertainty Quantification for Engineering & Life Sciences Does not take place this semester. Number of participants limited to 40. | W | 4 credits | 3G | P. Koumoutsakos | |
Abstract | Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology. | |||||
Objective | The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures. | |||||
Content | Topics that will be covered include: Uncertainty quantification under parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis. | |||||
Lecture notes | The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class. | |||||
Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes | |||||
Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling | |||||
151-0566-00L | Recursive Estimation | W | 4 credits | 2V + 1U | R. D'Andrea | |
Abstract | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||
Objective | Learn the basic recursive estimation methods and their underlying principles. | |||||
Content | Introduction 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 notes | Lecture notes available on course website: Link | |||||
Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | |||||
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 | To introduce students to the area of nonlinear systems and control, to familiarize them with tools for modelling and analysing nonlinear systems and to provide an overview of the various nonlinear controller design methods. | |||||
Objective | On 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. | |||||
Content | Virtually 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 only application of nonlinear analysis and synthesis methods will guarantee achievement of the desired objectives. During the past decades a number of practically applicable and 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 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. | |||||
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 |
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