Search result: Catalogue data in Spring Semester 2022
Robotics, Systems and Control Master | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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151-0116-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 credits | 4G | P. Koumoutsakos, S. M. Martin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Link Class notes, handouts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0306-00L | Visualization, Simulation and Interaction - Virtual Reality I | W | 4 credits | 4G | A. Kunz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Technology 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The 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. • Students are able to evaluate and select the most appropriate VR technology for a given task regarding: o Visualization technologies displays/projection systems/head-mounted displays o Tracking systems (inertia/optical/electromagnetic) o Interaction technologies (sensing gloves/real walking/eye tracking/touch/etc.) • Students are able to develop a VR application • Students are able to apply VR to industrial needs • Students will be able to apply the gained knowledge to a practical realization • Students will be able to compare different operation principles (VR/AR/MR/XR) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Introduction 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 notes | A complete version of the handout is also available in English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Voraussetzungen: keine 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0534-00L | Advanced Dynamics | W | 4 credits | 3V + 1U | P. Tiso | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 equivalent; Linear Algebra I-II, or equivalent. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0630-00L | Nanorobotics | W | 4 credits | 2V + 1U | S. Pané Vidal | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0636-00L | Soft and Biohybrid Robotics | W | 4 credits | 3G | R. Katzschmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Soft and biohybrid robots are emerging fields taking inspiration from Nature to create integrated robots that are inherently safer to interact with. You will be able to create the structures, actuators, sensors, models, controllers, and machine learning architectures exploiting the deformable nature of these robots. You will apply the learned principles to challenges of your research domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Learning Objective 1: Convert any robotics challenge into a functional soft robotic physical prototype Step 1: Formulate suitable functional requirements Step 2: Select actuator material Step 3: Design + fabricate suitable for the task Step 4: Controller for basic functionality Step 5: Learning Approach for complex robotic skills Learning Objective 2: Formulate control and learning frameworks to highly articulated robots in real life scenarios Step 1: Formulate the dynamic skills needed for the real life scenario Step 2: Pick or combine suitable control and learning frameworks given the robot at hand Step 3: Evaluate the control approach for a real life scenario Step 4: Modify and enhance the control approach and repeat the evaluation Learning Objective 3: Apply the principle of mechanical impedance and embodied intelligence to any research challenge within any domain Step 1: Identify the moving aspects of the problem Step 2: Choose and design the passive and actively-controlled degrees of freedom Step 3: Pick the actuation material based on suitability to your challenge Step 4: Design in detail multiple combinations of body and brain Step 5: Simulate, build, test, fail, and repeat this often and quickly until the soft robot works for simple settings Step 6: Upgrade and validate the robot for performances in real world conditions Learning Objective 4: Rethink approaches to robotics by moving towards designs made of living materials Step 1: Identify what problems could be easier to solve with a complex living material Step 2: Scout for available works that have potentially tackled the problem with a living material Step 3: Formulate a hypothesis for your new approach with a living material Step 4: Design a minimum viable prototype (MVP) that properly highlights your new approach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Students will cover a range of latest research insights on materials, fabrication technologies, and modeling approaches to design, simulate, and build soft and biohybrid robots. Part 1: Functional and intelligent materials for use in soft and biohybrid robotic applications Part 2: Design and design morphologies of soft robotic actuators and sensors Part 3: Fabrication techniques including 3D printing, casting, roll-to-roll, tissue engineering Part 4: Biohybrid robotics including microrobots and macrorobots; tissue engineering Part 5: Mechanical modeling including minimal parameter models, finite-element models and ML-based models Part 6: Closed-loop controllers of soft robots that exploit the robot's impedance and dynamics for locomotion and manipulation tasks Part 7: Machine Learning approaches to soft robotics, for design synthesis, modeling, and control A mandatory semester-long project will teach the participants to implement the skills and knowledge learned during the class by building their own soft robotic prototype or simulation. There is a mandatory pass/fail assignment to be submitted within the first two weeks of class to get a spot in the project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | All class materials including slides, recordings, class challenges infos, pre-reads, and tutorial summaries can be found on Moodle: Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | 1) Wang, Liyu, Surya G. Nurzaman, and Fumiya Iida. "Soft-material robotics." (2017). 2) Polygerinos, Panagiotis, et al. "Soft robotics: Review of fluid‐driven intrinsically soft devices; manufacturing, sensing, control, and applications in human‐robot interaction." Advanced Engineering Materials 19.12 (2017): 1700016. 3) Verl, Alexander, et al. Soft Robotics. Berlin, Germany:: Springer, 2015. 4) Cianchetti, Matteo, et al. "Biomedical applications of soft robotics." Nature Reviews Materials 3.6 (2018): 143-153. 5) Ricotti, Leonardo, et al. "Biohybrid actuators for robotics: A review of devices actuated by living cells." Science Robotics 2.12 (2017). 6) Sun, Lingyu, et al. "Biohybrid robotics with living cell actuation." Chemical Society Reviews 49.12 (2020): 4043-4069. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | dynamics, controls, intro to robotics Only for students at master or PhD level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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151-0634-00L | Perception and Learning for Robotics Number of participants limited to: 30 To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to Link for approval. | W | 4 credits | 9A | C. D. Cadena Lerma, J. J. Chung | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Applying Machine Learning methods for solving real-world robotics problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Slides will be made available to the students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Will be announced in the first lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | The students are expected to be familiar with material of the "Recursive Estimation" and the "Introduction to Machine Learning" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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). Registrations per e-mail is no longer accepted! | W | 4 credits | 2V + 2U | B. Nelson | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | 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 course is to practically and theoretically expose students to the fundamentals of mechatronic and robotic systems. Over the course of the semester, the lecture topics will include an overview of robotics, an introduction to different types of sensors and their use, the programming of microcontrollers and interfacing these embedded computers with the real world, signal filtering and processing, an introduction to different types of actuators and their use, an overview of computer vision, and forward and inverse kinematics. Throughout the course, students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems. By the end of the course, you will be able to independently choose, design and integrate these different building blocks into a working mechatronic system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The course consists of weekly lectures and lab sessions. The weekly topics are the following: 0. Course Introduction 1. C Programming 2. Sensors 3. Data Acquisition 4. Signal Processing 5. Digital Filtering 6. Actuators 7. Computer Vision and Kinematics 8. Modeling and Control 9. Review and Outlook The lecture schedule can be found on our course page on the MSRL website (Link) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | The students are expected to be familiar with C programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0660-00L | Model Predictive Control | W | 4 credits | 2V + 1U | M. Zeilinger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | - Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Simulation-based project providing practical experience with MPC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Script / lecture notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, M. Chli, N. Lawrance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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, environment perception, and probabilistic environment modeling, localization, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed across 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, environment perception, and probabilistic environment modeling, localization, 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-9904-00L | Applied Compositional Thinking for Engineers I | W | 4 credits | 3G | A. Censi, J. Lorand | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course is an introduction to Applied Category Theory and related techniques specifically targeted at persons with an applied background. We focus on the benefits of Applied Category Theory for thinking explicitly about abstraction and compositionality. The course will favor a computational/constructive approach, with concrete exercises in the Python language. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | In many domains of engineering and applied sciences it would be beneficial to think explicitly about abstraction and compositionality, to improve both the understanding of the problem and the design of the solution. However, the problem is that the type of math which could be useful to applications is not traditionally taught. Applied Category Theory is a new field of mathematics which could help a lot, but it is quite unreachable by non-mathematicians. Recently, many good options appeared for learning applied category theory; but none satisfy the two properties of 1) being approachable; and 2) highlighting how applied category theory can be used to formalize and solve concrete applied problems. This course will fill this gap. This course's goal is not to teach category theory for the sake of it. Rather, we will teach the "compositionality way of thinking"; category theory will be just the means towards it. This implies that the presentation of materials sometimes diverges from the usual way to teach category theory; and some common concepts might be de-emphasized in favor of more obscure concepts that are more useful for applications. The course will favor a computational/constructive approach: each concept is accompanied by concrete exercises in the programming language Python. Throughout the course, we will discuss many examples related to autonomous robotics, because it is at the intersection of many branches of engineering: we can talk about hardware (sensing, actuation, communication) and software (perception, planning, learning, control) and their composition. ### Intended learning outcomes ### The student is able to recognize algebraic structure for a familiar engineering domain. The student is able to translate such algebraic structure in a concrete implementation using a programming language for the purpose of solving a computational problem. The student can understand when there is a functorial structure between instances of a problem and solutions of the problem, and use such structure to write programs that use these compositionality structures to achieve either more elegance or efficiency (or both). The student is able to recognize structures in concrete scenarios at different levels of abstractions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | * Review of basic algebraic structures: - Sets and relations, relations - Semigroups, monoids, groups - Homomorphisms - Actions - Graphs * Posets and lattices * (Semi)Categories * Categories of algebraic structures * Categories useful in applications * Categories of processes and procedures * Isomorphisms * Universal properties * Functors * Embeddings * Monotone co-design theory * Monoidal categories, traced monoidal categories | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Slides and notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Course book: A. Censi, J. Lorand, G. Zardini, "Applied Compositional Thinking for Engineers" Available online at https://applied-compositional-thinking.engineering/ Note: book includes materials for both ACT4E I and ACT4E II (Fall 2022). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Algebra: at the level of a bachelor’s degree in engineering/computer science. Basics of Python programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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151-1115-00L | Aircraft Aerodynamics and Flight Mechanics Note: The previous course title in German until FS21 "Ausgewählte Kapitel der Flugtechnik". | W | 4 credits | 3G | M. Immer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Equations of motion. Aircraft flight performance, flight envelope. Aircraft static stability and control, longitudinal and lateral stability. Dynamic longitudinal and lateral stability. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | - Knowledge of methods to solve flight mechanic problems - To be able to apply basic methods for flight performance calculation and stability investigations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Equations of motion. Aircraft flight performance, flight envelope. Aircraft static stability and control, longitudinal and lateral stability. Dynamic longitudinal and lateral stability. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Mc 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 / Notice | Recommended: Lecture "Introduction to Aircraft and Car Aerodynamics" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
103-0848-00L | Industrial Metrology and Machine Vision Number of participants limited to 30. | W | 4 credits | 3G | K. Schindler, D. Salido Monzú | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course introduces contact and non-contact techniques for 3D coordinate, shape and motion determination as used for 3D inspection, dimensional control, reverse engineering, motion capture and similar industrial applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Understanding the physical basis of photographic sensors and imaging; familiarization with a broader view of image-based 3D geometry estimation beyond the classical photogrammetric approach; understanding the concepts of measurement traceability and uncertainty; acquiring an overview of general 3D image metrology including contact and non-contact techniques (coordinate measurement machines; optical tooling; laser-based high-precision instruments). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | CCD and CMOS technology; structured light and active stereo; shading models, shape from shading and photometric stereo; shape from focus; laser interferometry, laser tracker, laser radar; contact and non-contact coordinate measurement machines; optical tooling; measurement traceability, measurement uncertainty, calibration of measurement systems; 3d surface representations; case studies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture slides and further literature will be made available on the course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0248-00L | Power Electronic Systems II | W | 6 credits | 4G | J. Biela, F. Krismer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 for example high switching frequency AC/DC converters are presented. Simulation exercises, implemented in the simulation programme PLECS, 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 for example high switching frequency AC/DC converters or 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 PLECS, 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Introductory course on power electronics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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227-0518-10L | Design and Control of Electric Machines | W | 6 credits | 4G | D. Bortis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course covers modeling and control concepts of modern drive systems and provides a deeper understanding of the dynamic operation of electric machines. Different aspects arising in the design of electric drive systems are investigated. The exercises are used to consolidate the concepts discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The objective of this course is to convey knowledge on control strategies of different types of electric machines and on design principles of variable speed drive systems. A dynamic modeling of the electromechanical system is investigated, enabling the proper design of cascaded speed, torque/current controllers. Further objectives are the identification of machine parameters and a short insight into basic inverter circuits applied in advanced motor drive systems. Exercises are used to consolidate the presented theoretical concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | 1. Introduction to variable speed motor drive systems consisting of: - Electromechanical system - Power electronic system - Control system - Measurement system 2. Control structures and strategies of DC Machine/Synchronous machine/Asynchronous machine/Brushless DC machine. - Cascaded control - U/f Control - Slip Control - Field-oriented control 3. Dynamic Operation of electric machines - Dynamic modeling of electromechanical system - Controller types and design - Current/torque control - Speed control (Voltage control / Flux weakening) 4. Power electronic inverter circuits in variable speed drive systems - Voltage and current source inverter systems - Basic operation and pulse width modulation 5. Identification of machine parameters 6. Design principles of variable speed motor drives systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture notes and associated exercises including correct answers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Fundamentals of Electric Machines | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0528-00L | Power System Dynamics, Control and Operation | W | 6 credits | 4G | G. Hug | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. This course is dedicated to the dynamical properties of the electric power grid including how the system state is estimated, generation/load balance is ensured by frequency control and how the system reacts in case of faults in the system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The learning objectives of the course are to understand and be able to apply the dynamic modeling of power systems, to compute and discuss the actions of generators based on frequency control, to describe the workings of a synchronous machine and the implications on the grid, to describe and apply state estimation procedures, to discuss the IT infrastructure and protection algorithms in power systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. Consequently, the monitoring and operation of the electric power grid is a challenging task. The course starts with the introduction of general operational procedures and the discussion of state estimation which is an important tool to observe the state of the grid. The course is then dedicated to the modeling and studying of the dynamical properties of the electric power grid. Frequency control which ensures the generation/load balance in real time is the basis for real-time control and is presented in depth. For the analysis of how the system detects and reacts dynamically in fault situations, protection and dynamic models for synchronous machines are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture notes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0560-00L | Deep Learning for Autonomous Driving Number of participants limited to 80. | W | 6 credits | 3V + 2P | D. Dai, A. Liniger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Autonomous driving has moved from the realm of science fiction to a very real possibility during the past twenty years, largely due to rapid developments of deep learning approaches, automotive sensors, and microprocessor capacity. This course covers the core techniques required for building a self-driving car, especially the practical use of deep learning through this theme. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Students will learn about the fundamental aspects of a self-driving car. They will also learn to use modern automotive sensors and HD navigational maps, and to implement, train and debug their own deep neural networks in order to gain a deep understanding of cutting-edge research in autonomous driving tasks, including perception, localization and control. After attending this course, students will: 1) understand the core technologies of building a self-driving car; 2) have a good overview over the current state of the art in self-driving cars; 3) be able to critically analyze and evaluate current research in this area; 4) be able to implement basic systems for multiple autonomous driving tasks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | We will focus on teaching the following topics centered on autonomous driving: deep learning, automotive sensors, multimodal driving datasets, road scene perception, ego-vehicle localization, path planning, and control. The course covers the following main areas: I) Foundation a) Fundamentals of a self-driving car b) Fundamentals of deep-learning II) Perception a) Semantic segmentation and lane detection b) Depth estimation with images and sparse LiDAR data c) 3D object detection with images and LiDAR data d) Object tracking and Lane Detection III) Localization a) GPS-based and Vision-based Localization b) Visual Odometry and Lidar Odometry IV) Path Planning and Control a) Path planning for autonomous driving b) Motion planning and vehicle control c) Imitation learning and reinforcement learning for self driving cars The exercise projects will involve training complex neural networks and applying them on real-world, multimodal driving datasets. In particular, students should be able to develop systems that deal with the following problems: - Sensor calibration and synchronization to obtain multimodal driving data; - Semantic segmentation and depth estimation with deep neural networks ; - 3D object detection and tracking in LiDAR point clouds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The lecture slides will be provided as a PDF. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | This is an advanced grad-level course. Students must have taken courses on machine learning and computer vision or have acquired equivalent knowledge. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. All practical exercises will require basic knowledge of Python and will use libraries such as PyTorch, scikit-learn and scikit-image. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0694-00L | Game Theory and Control | W | 4 credits | 2V + 2U | S. Bolognani | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Game Theory is the study of strategic decision making, and was originally used to solve problems in economics. We study concepts and methods in non-cooperative game theory and show how these can be used to solve control design problems, emphasizing their possible use in control, robotics, and engineering applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Recognize control problems that can be formalized as noncooperative dynamic games, analyze these games to compute their Nash equilibria and to identify their most important properties. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Introduction to game theory, mathematical tools including convex optimization and dynamic programming, zero sum games in matrix and extensive form, pure and mixed strategies, nonzero sum games in normal and extensive form, numerical computation of mixed equilibrium strategies, Nash and Stackelberg equilibria, potential games, convex games, multi-stage games, behavioral strategies and informational properties for dynamic games, auction and VCG mechanisms, evolutionary games. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture notes will be made available via Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Basar, T. and Olsder, G. "Dynamic Noncooperative Game Theory," 2nd Edition, Society for Industrial and Applied Mathematics, 1998. Joao Hespanha "Noncooperative Game Theory: An introduction for engineers and computer scientists," Princeton University Press, 2017. Both books are available online and can be a useful reference during the course, but will not be strictly followed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Control Systems I (or equivalent). Necessary methods and concepts from optimization will be covered in the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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