Search result: Catalogue data in Spring Semester 2021

Robotics, Systems and Control Master Information
Core Courses
NumberTitleTypeECTSHoursLecturers
151-0116-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 credits4GP. Koumoutsakos, S. M. Martin
AbstractThis 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.
ObjectiveThe 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
ContentHigh 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 notesLink
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 / NoticeStudents must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)
151-0306-00LVisualization, Simulation and Interaction - Virtual Reality I Information W4 credits4GA. Kunz
AbstractTechnology 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).
ObjectiveThe 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)
ContentIntroduction 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 notesA complete version of the handout is also available in English.
Prerequisites / NoticeVoraussetzungen:
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
– Mündliche Einzelprüfung 30 Minuten
151-0534-00LAdvanced DynamicsW4 credits3V + 1UP. Tiso
AbstractLagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response
ObjectiveThis 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 notesLecture notes are produced in class and are downloadable right after each lecture.
LiteratureThe students will prepare their own notes. A copy of the lecture notes will be available.
Prerequisites / NoticeMechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent.
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction 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 notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
151-0593-00LEmbedded Control SystemsW4 credits6GJ. S. Freudenberg, M. Schmid Daners
AbstractThis course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device.
ObjectiveFamiliarize students with main architectural principles and concepts of embedded control systems.
ContentAn embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch.

Subjects covered in lectures and practical lab exercises include:
- The application of C-programming on a microprocessor
- Digital I/O and serial communication
- Quadrature decoding for wheel position sensing
- Queued analog-to-digital conversion to interface with the analog world
- Pulse width modulation
- Timer interrupts to create sampling time intervals
- System dynamics and virtual worlds with haptic feedback
- Introduction to rapid prototyping
Lecture notesLecture notes, lab instructions, supplemental material
Prerequisites / NoticePrerequisite courses are Control Systems I and Informatics I.

This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid Daners (E-Mail: Link)
After your reservation has been confirmed please register online at Link.

Detailed information can be found on the course website
Link
151-0630-00LNanorobotics Information W4 credits2V + 1US. Pané Vidal
AbstractNanorobotics 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.
ObjectiveThe 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.
151-0634-00LPerception and Learning for Robotics Restricted registration - show details
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.
W4 credits9AC. D. Cadena Lerma, J. J. Chung
AbstractThis 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.
ObjectiveApplying Machine Learning methods for solving real-world robotics problems.
ContentDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notesSlides will be made available to the students.
LiteratureWill be announced in the first lecture.
Prerequisites / NoticeThe 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.
151-0636-00LSoft and Biohybrid Robotics Information Restricted registration - show details W4 credits3GR. Katzschmann
AbstractSoft robotics takes inspiration from nature and produces systems that are inherently safer to interact with. The class teaches processes involved in creating the structures, actuators, sensors, mechanical models, controllers, and machine learning models exploiting the deformable structure of soft robots in challenging tasks.
(Class fully online via Zoom)
ObjectiveLearn about the processes involved in creating soft and biohybrid robotic structures, actuator, sensors, mechanical models, closed-loop controllers, and machine learning approaches. Understand how to exploit the structural impedance and the dynamics of soft and biohybrid robots in locomotion and object manipulation tasks. Demonstrated learned capabilities in either a simulation or a physical prototype built at home.
ContentStudents will gain experience on a range of soft technologies and a model-based approaches to design and simulation of soft continuum robots.
0) Semester-long take-home project requiring students to implement the skills and knowledge learned during the class by building their own soft robotic prototype or simulation
1) Functional and intelligent materials for use in soft and biohybrid robotic applications
2) Design and design morphologies of soft robotic actuators and sensors
3) Fabrication techniques including 3D printing, casting, roll-to-roll, tissue engineering
4) Mechanical modeling including minimal parameter models, finite-element models and ML-based models
5) Closed-loop controllers of soft robots that exploit the robot's impedance and dynamics for locomotion and manipulation tasks
6) Deep Learning approaches to soft robotics, for design synthesis, modeling, and control

(Class offered only online via Zoom, see Moodle for details)
Lecture notesAll class materials including slides, recordings, class challenges infos, pre-reads, and tutorial summaries can be found on Moodle: Link
Literature1) 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(Robot) dynamics, control systems, introduction to robotics, materials for engineers.
Only for students at master or PhD level.
Class size limitation is at 40 students.
151-0641-00LIntroduction to Robotics and Mechatronics Information Restricted registration - show details
Number of participants limited to 45.

Enrollment is only valid through registration on the MSRL website (Link). Registrations per e-mail is no longer accepted!
W4 credits2V + 2UB. Nelson, N. Shamsudhin
AbstractThe 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.
ObjectiveAn 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.
ContentThe 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 / NoticeThe students are expected to be familiar with C programming.
151-0660-00LModel Predictive Control Information W4 credits2V + 1UM. Zeilinger, A. Carron
AbstractModel 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 and covers advanced topics.
ObjectiveDesign 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
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticeOne 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).
151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, N. Lawrance
AbstractThe 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, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
ObjectiveThe 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, localizatoin, mapping and navigation.
Lecture notesThis 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.
LiteratureThis 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
151-9904-00LApplied Compositional Thinking for Engineers Information W4 credits3GE. Frazzoli, A. Censi, J. Lorand
AbstractThis course is an introduction to applied category theory specifically targeted at persons with an engineering 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.
ObjectiveIn many domains of engineering 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 engineers is not traditionally taught.

Applied category theory could help a lot, but it is quite unreachable by the average engineer. 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 problems of interest to engineers.

This course will fill this gap. This course's goal is not to teach category theory for the sake of it. Rather, we want to teach the "compositionality way of thinking" to engineers; 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 to an engineer.

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.
Content## Intended learning outcomes

# Algebraic structures

The student is able to recognize algebraic structure for a familiar engineering domain. In particular we will recall
the following structures: monoid, groups, posets, monoidal posets, graphs.

The student is able to translate such algebraic structure in a concrete implementation using the Python language for the purpose of solving a computational problem.

# Categories and morphisms

The student is able to recognize categorical structure for a familiar engineering domain, understand the notion of object, morphism, homsets, and the properties of associativity and unitality.

The student is able to quickly spot non-categories (formalizations in which one of the axioms fails, possibly in a subtle way) and is informed that there exist possible generalizations (not studied in the course).

The student is able to translate a categorical structure into a concrete implementation using the Python language.

The student is able to recognize the categorical structure in the basic algebraic structures previously considered.

The student is able to use string diagrams to represent morphisms; and to write a Python program to draw such a representation.

# Products, coproducts, universality

# Recognizing and using additional structure

The student is able to spot the presence of the following structures: Monoidal structure, Feedback structure (Trace),
Locally posetal/lattice structure , Dagger/involutive structure.

# Functorial structure.

The student is able to recognize functorial structures in a familiar engineering domain.

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 ladder of abstractions

The student is able to think about scenarios in which one can climb the ladder of abstractions. For example, the morphisms in a category can be considered objects in another category.

# Compact closed structure.

# Co-design

The student knows co-design theory (boolean profunctors + extensions) and how to use it to formalize design problems in their area of expertise.

The student knows how to use the basics of the MCPD language and use it to solve co-design problems.

# Rosetta stone

The student understands explicitly the connection between logic and category theory and can translate concepts back and forth.

The student understands explicitly the constructive nature of the presentation of category theory given so far.

The student is able to understand what is an "equational theory" and how to use it concretely.

The student understands the notion of substructural logics; the notion of polycategories; and linear logic. Mention of *-autonomous categories.

The student can translate the above in an implementation.

# Monadic structure

The student is able to recognize a monadic structure in the problem.

# Operads and operad-like structures.
Lecture notesSlides and notes will be provided.
LiteratureB. Fong, D.I. Spivak, Seven Sketches in Compositionality: An Invitation to Applied Category Theory (Link)

A. Censi, D. I. Spivak, J. Tan, G. Zardini, Mathematical Foundations of Engineering Co-Design (Own manuscript, to be published)
Prerequisites / NoticeAlgebra: at the level of a bachelor’s degree in engineering.

Analysis: ODEs, dynamical systems.

Familiarity with basic physics, electrical engineering, mechanical engineering, mechatronics concepts (at the level of bachelor's degree in engineering).

Basics of Python programming.
151-1115-00LAircraft Aerodynamics and Flight MechanicsW4 credits3GJ. Wildi
AbstractEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight test. Wind tunnel test.
Objective- Knowledge of methods to solve flight mechanic problems
- To be able to apply basic methods for flight performence calculation and stability investigations
- Basic knowledge of flight and wind tunnel tests and test evaluation methods
ContentEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight testing. Wind tunnel testing.
Lecture notesAusgewählte Kapitel der Flugtechnik (J. Wildi)
LiteratureMc 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 / NoticeRecommended: Lecture 'Basics of Aircraft und Vehicle Aerodynamics' (FS)
101-0521-10LMachine Learning for Predictive Maintenance Applications Restricted registration - show details
The number of participants in the course is limited to 25 students.

Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words):
-How does this course fit to the other courses you have attended so far?
-How does the course support you in achieving your goal?
The following link can be used to upload the documents.
Link
W8 credits4GO. Fink
AbstractThe course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime.
ObjectiveStudents will
- be able to understand the main challenges faced by predictive maintenance systems
- learn to extract relevant features from condition monitoring data
-learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics
-learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples.
- learn to design end-to-end machine learning algorithms for fault detection and diagnostics
-be able to evaluate the performance of the applied algorithms.

At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data.
ContentEarly and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing.
This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems.

Specific topics include:

-Introduction to condition monitoring and predictive maintenance systems
-Feature extraction and selection methodology
-Machine learning algorithms for fault detection and fault isolation
-End-to-end learning architectures (including feature learning) for fault detection and fault isolation
-Unsupervised and semi-supervised learning algorithms for predictive maintenance
-Machine learning algorithms for prediction of the remaining useful life
-Performance evaluation
-Predictive maintenance systems at fleet level
-Domain adaptation for fault diagnostics
-Introduction to decision support systems for maintenance applications
Lecture notesSlides and other materials will be available online.
LiteratureRelevant scientific papers will be discussed in the course.
Prerequisites / NoticeStrong analytical skills.
Programming skills in python are strongly recommended.
103-0848-00LIndustrial Metrology and Machine Vision Restricted registration - show details
Number of participants limited to 30.
W4 credits3GK. Schindler, D. Salido Monzú
AbstractThis 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.
ObjectiveUnderstanding 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).
ContentCCD 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 notesLecture slides and further literature will be made available on the course webpage.
227-0207-00LNonlinear Systems and Control Information
Prerequisite: Control Systems (227-0103-00L)
W6 credits4GE. Gallestey Alvarez, P. F. Al Hokayem
AbstractIntroduction 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.
ObjectiveOn 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.
ContentVirtually 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 notesAn english manuscript will be made available on the course homepage during the course.
LiteratureH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Prerequisites / NoticePrerequisites: Linear Control Systems, or equivalent.
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis 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 notesThe slides of the lecture are available to download.
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0224-00LStochastic Systems
Does not take place this semester.
W4 credits2V + 1Uto be announced
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic 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 notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0248-00LPower Electronic Systems II Information W6 credits4GJ. W. Kolar
AbstractThis 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.
ObjectiveThe 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.
ContentConverter 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 notesLecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.
Prerequisites / NoticePrerequisites: Introductory course on power electronics.
227-0518-10LDesign and Control of Electric MachinesW6 credits4GD. Bortis
AbstractThis 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.
ObjectiveThe 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.
Content1. 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 notesLecture notes and associated exercises including correct answers
Prerequisites / NoticePrerequisites: Fundamentals of Electric Machines
227-0528-00LPower System Dynamics, Control and Operation Information W6 credits4GG. Hug
AbstractThe 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. The course includes two excursions.
ObjectiveThe 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.
ContentThe 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 notesLecture notes. WWW pages.
227-0560-00LDeep Learning for Autonomous Driving Information Restricted registration - show details
Registration in this class requires the permission of the instructors.
Class size will be limited to 80 students.
Please send an email to Dengxin Dai <Link> about your courses/projects that are related to machine learning, computer vision, and Robotics.
W6 credits3V + 2PD. Dai, A. Liniger
AbstractAutonomous 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.
ObjectiveStudents 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.
ContentWe 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 notesThe lecture slides will be provided as a PDF.
Prerequisites / NoticeThis 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.
227-0694-00LGame Theory and ControlW4 credits2V + 2US. Bolognani
AbstractGame 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.
ObjectiveFormulate an optimal control problem as a noncooperative dynamic game, compute mixed and behavioural strategies for different equilibria.
ContentIntroduction 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.
Lecture notesWill be made available from SPOD or course webpage.
LiteratureBasar, T. and Olsder, G. Dynamic Noncooperative Game Theory, 2nd
Edition, Society for Industrial and Applied Mathematics, 1998. Available through ETH Bibliothek directly at Link.
Prerequisites / NoticeControl Systems I (or equivalent). Necessary methods and concepts from optimization will be covered in the course.
227-0696-00LPredictive Control of Power Electronics SystemsW6 credits2V + 2UT. Geyer
AbstractBridging the gap between modern control methods and power electronics, this course focuses on predictive control methods applied to power electronics systems. This includes model predictive control methods with and without a modulator. This course targets power electronics and control students.
Objective- Knowledge of modern time-domain control methods applied to three-phase converters and their corresponding loads. These control methods include model predictive control (MPC) and deadbeat 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.
Content- Review of mathematical modelling and time-domain control methods (particularly MPC and deadbeat control).
- 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.
Lecture notesThe lecture is based on the book "Model Predictive Control of High Power Converters and Industrial Drives" by T. Geyer. Additional notes will be made available in the class.
Prerequisites / Notice- Power Electronic Systems I
- Control Systems I (Regelsysteme I)
- Signal and System Theory II
252-0220-00LIntroduction to Machine Learning Information Restricted registration - show details
Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact Link
W8 credits4V + 2U + 1AA. Krause, F. Yang
AbstractThe course introduces the foundations of learning and making predictions based on data.
ObjectiveThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor
- Neural networks (backpropagation, regularization, convolutional neural networks)
- Unsupervised learning (k-means, PCA, neural network autoencoders)
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
LiteratureTextbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Prerequisites / NoticeDesigned to provide a basis for following courses:
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
252-0312-00LUbiquitous Computing Information W6 credits2V + 3AC. Holz
AbstractUbiquitous Computing means interacting with information and with each other anywhere, mediated through miniature technology everywhere. We will investigate the technical aspects of Ubicomp, particularly sensing, processing, and sense making: input (touch & gesture), activity, monitoring cardiovascular health and neurological conditions, context & location sensing, affective computing.
ObjectiveThe course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them.

High-level:
– input modalities for interactive systems (touch, gesture)
– "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations)
– health monitoring (basic cardiovascular physiology)
– location (GPS, urban simulations, smart cities and development)
– affective computing (emotions, mood, personality)

Low-level:
– sampling (Shannon Nyquist) and filtering (FIR, IIR), time and frequency domains (Fourier transforms)
– cross-modal sensor systems, signal synchronization and correlation
– event detection, classification, prediction using basic signal processing as well as learning-based methods
– sensor types: optical, mechanical/acoustic, electromagnetic

– signals modalities and processing of: application (modalities/methods)
* touch detection (resistive sensing, capacitive sensing, diffuse illumination/DI, spectral reflections, frustrated total internal reflection/FTIR, fingerprint scanning, surface-acoustic waves)
* gesture recognition (inertial sensing through accelerometers, gyroscopes)
* activity detection and tracking (inertial, acoustic, vibrotactile for classification, counting, vibrometry)
* occupation and use (electricity monitoring, water consumption, single-point sensing)
* cardiovascular (electrocardioagraphy, photoplethysmography, pulse oximetry, ballistocardiography, blood pressure, pulse transit time, bio impedance)
* affective computing (heart rate variability, R-R intervals, electrodermal activity, sympathetic tone, facial expressions)
* neurological (fatigue, fatigability)
* location (GPS, BLE, Wifi)
Content"The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it" — Mark Weiser, 1991.

This is the premise of Ubiquitous Computing, a vision that is slowly becoming reality as everything is a device and we can interact with information and with each other anywhere, mediated through miniature technology. Along with this change, interaction modalities have changed, too, from explicit input on keyboards and mice to implicit and passively observed input through sensors in the environment (e.g., speakers, cameras, temperature/occupancy detectors) and those we now wear on our bodies (e.g., health sensors, activity sensors, miniature computers we call smartwatches).

In this course, we will look at the technical side of Ubicomp, particularly
– sensing (incl. 'signals', sampling, data acquisition methods, controlled user studies, uncontrolled studies in-the-wild),
– processing (incl. frequencies, feature extraction, detection), and
– sense making: input sensing (touch & gesture), activity sensing (motion), monitoring cardiovascular health, affective state, neurological conditions (with basics on cardiovascular physiology + PPG, PulseOx, ECG, EDA, BCG, SCG, HRV, BioZ, IPG, PAT, PTT), context & location sensing (GPS/Wifi, motion).

Lectures will be accompanied by practical sessions that focus on sensor modalities and signal processing. Here, we will work on existing data sets and devise methods to record our own data for processing and prediction purposes.

A series of reading assignments, covering both well-established publications in Ubicomp as well as emerging results and methods, will bridge the fundamentals and topics taught in class to academic research and real-world problems.

More information on the course site: Link
Lecture notesCopies of slides will be made available. Lectures will be recorded and made available online.

More information on the course site: Link
LiteratureWill be provided in the lecture. To put you in the mood:
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
252-0526-00LStatistical Learning Theory Information W8 credits3V + 2U + 2AJ. M. Buhmann, C. Cotrini Jimenez
AbstractThe course covers advanced methods of statistical learning:

- Variational methods and optimization.
- Deterministic annealing.
- Clustering for diverse types of data.
- Model validation by information theory.
ObjectiveThe course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning.
Content- Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing.

- Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures.

- Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation.

- Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models.
Lecture notesA draft of a script will be provided. Lecture slides will be made available.
LiteratureHastie, 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
Prerequisites / NoticeKnowledge of machine learning (introduction to machine learning and/or advanced machine learning)
Basic knowledge of statistics.
252-0579-00L3D Vision Information W5 credits3G + 1AM. Pollefeys, V. Larsson
AbstractThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
ObjectiveAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
ContentThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
263-5806-00LComputational Models of Motion Information W8 credits2V + 2U + 3AS. Coros, M. Bächer, B. Thomaszewski
AbstractThis course covers fundamentals of physics-based modelling and numerical optimization from the perspective of character animation and robotics applications. The methods discussed in class derive their theoretical underpinnings from applied mathematics, control theory and computational mechanics, and they will be richly illustrated using examples ranging from locomotion controllers and crowd simula
ObjectiveStudents will learn how to represent, model and algorithmically control the behavior of animated characters and real-life robots. The lectures are accompanied by programming assignments (written in C++) and a capstone project.
ContentOptimal control and trajectory optimization; multibody systems; kinematics; forward and inverse dynamics; constrained and unconstrained numerical optimization; mass-spring models for crowd simulation; FEM; compliant systems; sim-to-real; robotic manipulation of elastically-deforming objects.
Prerequisites / NoticeExperience with C++ programming, numerical linear algebra and multivariate calculus. Some background in physics-based modeling, kinematics and dynamics is helpful, but not necessary.
376-1217-00LRehabilitation Engineering I: Motor FunctionsW4 credits2V + 1US. Raspopovic, M. Xiloyannis
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.
ObjectiveProvide 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.
LiteratureIntroductory 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 / NoticeTarget 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
227-0690-12LAdvanced Topics in Control (Spring 2021)
New topics are introduced every year.
W4 credits2V + 2UF. Dörfler, M. Hudoba de Badyn, W. Mei
AbstractAdvanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor. During the spring of 2020, the course will cover a range of topics in distributed systems control.
ObjectiveBy the end of this course you will have developed a sound and versatile toolkit to tackle a range of problems in network systems and distributed systems control. In particular, we will develop the methodological foundations of algebraic graph theory, consensus algorithms, and multi-agent systems. Building on top of these foundations we cover a range of problems in epidemic spreading over networks, swarm robotics, sensor networks, opinion dynamics, distributed optimization, and electrical network theory.
ContentDistributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are electric power grids, swarm robotics, sensor networks, and epidemic spreading over networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, computing, and control capabilities. This course covers algebraic graph theory, consensus algorithms, stability of network systems, distributed optimization, and applications in various domains.
Lecture notesA complete set of lecture notes and slides will be provided.
LiteratureThe course will be largely based on the following set of lecture notes co-authored by one of the instructors: Link
Prerequisites / NoticeSufficient mathematical maturity, in particular in linear algebra and dynamical systems.