Search result: Catalogue data in Autumn Semester 2021

Mechanical Engineering Master Information
Core Courses
Robotics, Systems and Control
The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.
NumberTitleTypeECTSHoursLecturers
151-0107-20LHigh Performance Computing for Science and Engineering (HPCSE) IW4 credits4GP. Koumoutsakos, S. M. Martin
AbstractThis course gives an introduction into algorithms and numerical methods for parallel computing on shared and distributed memory architectures. The algorithms and methods are supported with problems that appear frequently in science and engineering.
ObjectiveWith manufacturing processes reaching its limits in terms of transistor density on today’s computing architectures, efficient utilization of computing resources must include parallel execution to maintain scaling. The use of computers in academia, industry and society is a fundamental tool for problem solving today while the “think parallel” mind-set of developers is still lagging behind.

The aim of the course is to introduce the student to the fundamentals of parallel programming using shared and distributed memory programming models. The goal is on learning to apply these techniques with the help of examples frequently found in science and engineering and to deploy them on large scale high performance computing (HPC) architectures.
Content1. Hardware and Architecture: Moore’s Law, Instruction set architectures (MIPS, RISC, CISC), Instruction pipelines, Caches, Flynn’s taxonomy, Vector instructions (for Intel x86)

2. Shared memory parallelism: Threads, Memory models, Cache coherency, Mutual exclusion, Uniform and Non-Uniform memory access, Open Multi-Processing (OpenMP)

3. Distributed memory parallelism: Message Passing Interface (MPI), Point-to-Point and collective communication, Blocking and non-blocking methods, Parallel file I/O, Hybrid programming models

4. Performance and parallel efficiency analysis: Performance analysis of algorithms, Roofline model, Amdahl’s Law, Strong and weak scaling analysis

5. Applications: HPC Math libraries, Linear Algebra and matrix/vector operations, Singular value decomposition, Neural Networks and linear autoencoders, Solving partial differential equations (PDEs) using grid-based and particle methods
Lecture notesLink
Class notes, handouts
Literature• An Introduction to Parallel Programming, P. Pacheco, Morgan Kaufmann
• Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press
• Computer Organization and Design, D.H. Patterson and J.L. Hennessy, Morgan Kaufmann
• Vortex Methods, G.H. Cottet and P. Koumoutsakos, Cambridge University Press
• Lecture notes
Prerequisites / NoticeStudents should be familiar with a compiled programming language (C, C++ or Fortran). Exercises and exams will be designed using C++. The course will not teach basics of programming. Some familiarity using the command line is assumed. Students should also have a basic understanding of diffusion and advection processes, as well as their underlying partial differential equations.
151-0325-00LPlanning and Decision Making for Autonomous RobotsW4 credits2V + 1UE. Frazzoli
AbstractPlanning safe and efficient motions for robots in complex environments, often shared with humans and other robots, is a difficult problem combining discrete and continuous mathematics, as well as probabilistic, game-theoretic, and learning aspects. This course will cover the algorithmic foundations of motion planning, with an eye to real-world implementation issues.
ObjectiveThe students will learn how to design and implement state-of-the-art algorithms for planning the motion of robots executing challenging tasks in complex environments.
ContentDiscrete planning, shortest path problems. Planning under uncertainty. Game-theoretic planning. Geometric Representations. Configuration space. Grids, lattices, visibility graphs. Sampling-based methods. Potential and Navigation functions. Mathematical Programming. Local and global optimization, convex relaxations. Planning with limited information. Multi-agent Planning.
Lecture notesCourse notes and other education material will be provided for free in an electronic form.
LiteratureThere is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."
Prerequisites / NoticeStudents should have taken basic courses in optimization, control systems, probability theory, and should be familiar with basic programming (e.g., Python, and/or C/C++). Previous exposure to robotic systems is a definite advantage.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
151-0371-00LAdvanced Model Predictive Control Restricted registration - show details
Number of participants limited to 60.
W4 credits2V + 1UM. Zeilinger, A. Carron, L. Hewing, J. Köhler
AbstractModel predictive control (MPC) has established itself as a powerful control technique for complex systems under state and input constraints. This course discusses the theory and application of recent advanced MPC concepts, focusing on system uncertainties and safety, as well as data-driven formulations and learning-based control.
ObjectiveDesign, implement and analyze advanced MPC formulations for robust and stochastic uncertainty descriptions, in particular with data-driven formulations.
ContentTopics include
- Review of Bayesian statistics, stochastic systems and Stochastic Optimal Control
- Nominal MPC for uncertain systems (nominal robustness)
- Robust MPC
- Stochastic MPC
- Set-membership Identification and robust data-driven MPC
- Bayesian regression and stochastic data-driven MPC
- MPC as safety filter for reinforcement learning
Lecture notesLecture notes will be provided.
Prerequisites / NoticeBasic courses in control, advanced course in optimal control, basic MPC course (e.g. 151-0660-00L Model Predictive Control) strongly recommended.
Background in linear algebra and stochastic systems recommended.
151-0532-00LNonlinear Dynamics and Chaos I Information W4 credits2V + 2UG. Haller
AbstractBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
ObjectiveThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Content(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
Lecture notesThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Prerequisites / Notice- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
151-0563-01LDynamic Programming and Optimal Control Information W4 credits2V + 1UR. D'Andrea
AbstractIntroduction to Dynamic Programming and Optimal Control.
ObjectiveCovers the fundamental concepts of Dynamic Programming & Optimal Control.
ContentDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteratureDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Prerequisites / NoticeRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
151-0567-00LEngine Systems Information W4 credits3GC. Onder
AbstractIntroduction to current and future engine systems and their control systems
ObjectiveIntroduction to methods of control and optimization of dynamic systems. Application to real engines. Understand the structure and behavior of drive train systems and their quantitative descriptions.
ContentPhysical description and mathematical models of components and subsystems (mixture formation, load control, supercharging, emissions, drive train components, etc.).
Case studies of model-based optimal design and control of engine systems with the goal of minimizing fuel consumption and emissions.
Lecture notesIntroduction to Modeling and Control of Internal Combustion Engine Systems
Guzzella Lino, Onder Christopher H.
2010, Second Edition, 354 p., hardbound
ISBN: 978-3-642-10774-0
Prerequisites / NoticeCombined homework and testbench exercise (air-to-fuel-ratio control or idle-speed control) in groups
151-0569-00LVehicle Propulsion Systems Information W4 credits3GC. Onder, P. Elbert
AbstractIntroduction to current and future propulsion systems and the electronic control of their longitudinal behavior
ObjectiveIntroduction to methods of system optimization and controller design for vehicles. Understanding the structure and working principles of conventional and new propulsion systems. Quantitative descriptions of propulsion systems
ContentUnderstanding of physical phenomena and mathematical models of components and subsystems (manual, automatic and continuously variable transmissions, energy storage systems, electric drive trains, batteries, hybrid systems, fuel cells, road/wheel interaction, automatic braking systems, etc.).

Presentation of mathematical methods, CAE tools and case studies for the model-based design and control of propulsion systems with the goal of minimizing fuel consumption and emissions.
Lecture notesVehicle Propulsion Systems --
Introduction to Modeling and Optimization
Guzzella Lino, Sciarretta Antonio
2013, X, 409 p. 202 illus., Geb.
ISBN: 978-3-642-35912-5
Prerequisites / NoticeLectures of Prof. Dr. Ch. Onder and Dr. Ph. Elbert are also possible to be held in German.
151-0573-00LSystem Modeling Information W4 credits2V + 1UL. Guzzella
AbstractIntroduction to system modeling for control. Generic modeling approaches based on first principles, Lagrangian formalism, energy approaches and experimental data. Model parametrization and parameter estimation. Basic analysis of linear and nonlinear systems.
ObjectiveLearn how to mathematically describe a physical system or a process in the form of a model usable for analysis and control purposes.
ContentThis class introduces generic system-modeling approaches for control-oriented models based on first principles and experimental data. The class will span numerous examples related to mechatronic, thermodynamic, chemistry, fluid dynamic, energy, and process engineering systems. Model scaling, linearization, order reduction, and balancing. Parameter estimation with least-squares methods. Various case studies: loud-speaker, turbines, water-propelled rocket, geostationary satellites, etc. The exercises address practical examples.
Lecture notesThe handouts in English will be available in digital form.
LiteratureA list of references is included in the handouts.
CompetenciesCompetencies
Concepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
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
151-0601-00LTheory of Robotics and Mechatronics Information W4 credits3GP. Korba, S. Stoeter
AbstractThis course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
ObjectiveRobotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
ContentAn introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
Lecture notesavailable.
151-0604-00LMicrorobotics Information W4 credits3GB. Nelson, N. Shamsudhin
AbstractMicrorobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination.
ObjectiveThe objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field.
ContentMain topics of the course include:
- Scaling laws at micro/nano scales
- Electrostatics
- Electromagnetism
- Low Reynolds number flows
- Observation tools
- Materials and fabrication methods
- Applications of biomedical microrobots
Lecture notesThe powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically.
Prerequisites / NoticeThe lecture will be taught in English.
151-0632-00LVision Algorithms for Mobile Robotics (University of Zurich) Information
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: DINF2039

Mind the enrolment deadlines at UZH:
Link
W6 credits2V + 2UD. Scaramuzza
AbstractFor a robot to be autonomous, it has to perceive and understand the world around it. This course introduces you to the key computer vision algorithms used in mobile robotics, such as feature extraction, structure from motion, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Hololens, Oculus Quest, and the NASA Mars rovers).
ObjectiveLearn the fundamental computer vision algorithms used in mobile robotics, in particular: filtering, feature extraction, structure from motion, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry and Simultaneous Localization And Mapping (SLAM) (the algorithms behind Hololens, Facebook-Oculus Quest, and the NASA Mars rovers).
ContentEach lecture will be followed by a lab session where you will learn to implement a building block of a visual odometry algorithm in Matlab. By the end of the course, you will integrate all these building blocks into a working visual odometry algorithm.
Lecture notesLecture slides will be made available on the course official website: Link
Literature[1] Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010.
[2] Robotics Vision and Control: Fundamental Algorithms, by Peter Corke 2011.
[3] An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
[4] Multiple view Geometry, by R. Hartley and A. Zisserman.
[5] Introduction to autonomous mobile robots 2nd Edition, by R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, February, 2011
Prerequisites / NoticeFundamentals of algebra, geomertry, matrix calculus, and Matlab programming.

Note: If you are interested in taking UZH courses, you must register as an incoming mobility student at UZH. For details, see as follows:

UZH course enrollment for ETH student at University of Zurich (UZH) > Mobility within Switzerland – Incoming > Module Mobility: The easiest way to take individual modules/courses to supplement your studies at your home university is with module mobility. This option is not available to students who have dropped out of their home university or have been definitely excluded or banned from the relevant a program > Application and Deadlines: Applications are submitted via the UZH application portal (Link).
151-0655-00LSkills for Creativity and InnovationW4 credits3GI. Goller, C. Kobe
AbstractThis lecture aims to enhance the knowledge and competency of students regarding their innovation capability. An overview on prerequisites of and different skills for creativity and innovation in individual & team settings is given. The focus of this lecture is clearly on building competencies - not just acquiring knowledge.
Objective- Basic knowledge about creativity and skills
- Knowledge about individual prerequisites for creativity
- Development of individual skills for creativity
- Knowledge about teams
- Development of team-oriented skills for creativity
- Knowledge and know-how about transfer to idea generation teams
ContentBasic knowledge about creativity and skills:
- Introduction into creativity & innovation: definitions and models

Knowledge about individual prerequisites for creativity:
- Personality, motivation, intelligence

Development of individual skills for creativity:
- Focus on creativity as problem analysis & solving
- Individual skills in theoretical models
- Individual competencies: exercises and reflection

Knowledge about teams:
- Definitions and models
- Roles in innovation processes

Development of team-oriented skills for creativity:
- Idea generation and development in teams
- Cooperation & communication in innovation teams

Knowledge and know-how about transfer to idea generation teams:
- Self-reflection & development planning
- Methods of knowledge transfer
Lecture notesSlides, script and other documents will be distributed via moodle.ethz.ch
(access only for students registered to this course)
LiteratureGoller, I. & Bessant, J. (2017). Creativity for Innovation Management. Routledge. (ISBN-13: 978-1138641327)
As well as material handed out in the lecture
151-0727-00LColloquium on Manufacturing TechnologyW4 credits3KK. Wegener, A. Kunz
AbstractFuture training on selected current topics of the manufacturing technology. Per afternoon a selected topic is presented in several lectures, by the majority by experts from the industry. The students prepare a summary of the lectures given and prepare themselves on the basis of these lectures and own information search.
ObjectiveContious further training to current topics of the manufacturing technique. Exchange of experience and knowledge with the industry and other universities.
ContentSelected actual topics on manufacturing methods and tools, machine tools, NC-control and drives, components and measuring methods and devices. Topics are changing every year.
Lecture notesno Script
Prerequisites / Notice- Students must have participated and passed the courses Manufacturing, Production Machines I and Forming Technology III - Forming Processes.

- Further training with specialized lectures and large participation from the industry.
Language: Help for English speaking students on request.
151-0851-00LRobot Dynamics Information Restricted registration - show details W4 credits2V + 2UM. Hutter, R. Siegwart
AbstractWe will provide an overview on how to kinematically and dynamically model typical robotic systems such as robot arms, legged robots, rotary wing systems, or fixed wing.
ObjectiveThe primary objective of this course is that the student deepens an applied understanding of how to model the most common robotic systems. The student receives a solid background in kinematics, dynamics, and rotations of multi-body systems. On the basis of state of the art applications, he/she will learn all necessary tools to work in the field of design or control of robotic systems.
ContentThe course consists of three parts: First, we will refresh and deepen the student's knowledge in kinematics, dynamics, and rotations of multi-body systems. In this context, the learning material will build upon the courses for mechanics and dynamics available at ETH, with the particular focus on their application to robotic systems. The goal is to foster the conceptual understanding of similarities and differences among the various types of robots. In the second part, we will apply the learned material to classical robotic arms as well as legged systems and discuss kinematic constraints and interaction forces. In the third part, focus is put on modeling fixed wing aircraft, along with related design and control concepts. In this context, we also touch aerodynamics and flight mechanics to an extent typically required in robotics. The last part finally covers different helicopter types, with a focus on quadrotors and the coaxial configuration which we see today in many UAV applications. Case studies on all main topics provide the link to real applications and to the state of the art in robotics.
Prerequisites / NoticeThe contents of the following ETH Bachelor lectures or equivalent are assumed to be known: Mechanics and Dynamics, Control, Basics in Fluid Dynamics.
151-0905-00LMedical Technology Innovation - From Concept to Clinics Restricted registration - show details W4 credits3PI. Herrmann
AbstractProject-oriented learning on how to develop technological solutions to address unmet clinical needs.
ObjectiveAfter completing the course, you will be able to effectively collaborate with medical doctors in order to identify important unmet clinical needs. You will be able to ideate and develop appropriate engineering solutions and implementation strategies for real-world clinical problems. This lecture aims to prepare you for typical engineering challenges in the real-world where - in addition to the development of an elegant solution -interdisciplinary team work and effective communication play a key role.
Literaturewill be available on the moodle.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationassessed
Leadership and Responsibilityassessed
Self-presentation and Social Influence assessed
Sensitivity to Diversityassessed
Negotiationassessed
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection assessed
Self-direction and Self-management assessed
151-0917-00LMass TransferW4 credits2V + 2US. E. Pratsinis, V. Mavrantzas, C.‑J. Shih
AbstractThis course presents the fundamentals of transport phenomena with emphasis on mass transfer. The physical significance of basic principles is elucidated and quantitatively described. Furthermore the application of these principles to important engineering problems is demonstrated.
ObjectiveThis course presents the fundamentals of transport phenomena with emphasis on mass transfer. The physical significance of basic principles is elucidated and quantitatively described. Furthermore the application of these principles to important engineering problems is demonstrated.
ContentFick's laws; application and significance of mass transfer; comparison of Fick's laws with Newton's and Fourier's laws; derivation of Fick's 2nd law; diffusion in dilute and concentrated solutions; rotating disk; dispersion; diffusion coefficients, viscosity and heat conduction (Pr and Sc numbers); Brownian motion; Stokes-Einstein equation; mass transfer coefficients (Nu and Sh numbers); mass transfer across interfaces; Analogies for mass-, heat-, and momentum transfer in turbulent flows; film-, penetration-, and surface renewal theories; simultaneous mass, heat and momentum transfer (boundary layers); homogeneous and heterogeneous reversible and irreversible reactions; diffusion-controlled reactions; mass transfer and first order heterogeneous reaction. Applications.
LiteratureCussler, E.L.: "Diffusion", 3nd edition, Cambridge University Press, 2009.
Prerequisites / NoticeStudents attending this highly-demanding course are expected to allocate sufficient time within their weekly schedule to successfully conduct the exercises.
151-1116-00LIntroduction to Aircraft and Car Aerodynamics
Note: The previous course title in German until HS20 "Einführung in Flug- und Fahrzeugaerodynamik".
W4 credits3GM. Immer, F. Schröder
AbstractAircraft aerodynamics: Atmosphere; aerodynamic forces (lift, drag); thrust.
Vehicle aerodynamics: Aerodynamic and mass forces, drag, lift, car aerodynamics and performence. Passenger cars, trucks, racing cars.
ObjectiveAn introduction to the basic principles and interrelationships of aircraft and automotive aerodynamics.
To understand the basic relations of the origin of aerodynamic forces (ie lift, drag). To quantify the aerodynamic forces for basic configurations of aircraft and car components.
Illustration of the intrinsic problems and results using examples.
Using experimental and theoretical methods to illustrate possibilities and limits.
ContentAircraft aerodynamics: atmosphere, aerodynamic forces (ascending force: profile, wings. Resistance, residual resistance, induced resistance); thrust (overview of the propulsion system, aerodynamics of the propellers), introduction to static longitudinal stability.

Automobile aerodynamics: Basic principles: aerodynamic force and the force of inertia, resistance, drive, aerodynamic and driving performance. Cars commercial vehicles, racing cars.
Lecture notesPreparation materials & slides are provided prior to each class
LiteratureAircraft Aerodynamics:
- Anderson Jr, John D: Introduction to Flight, Mc Graw Hill, Ed 06, 2007; ISBN: 9780073529394
- Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics, John Wiley and Sons, 1979
- Wilcox, David C, Basic Fluid Mechanics. DCW Industries, Inc., 1997
- Schlichting,H. und Truckenbrodt, E: Aerodynamik des Flugzeuges (Bd I und II), Springer Verlag, 1960
- Abbott, I. and van Doenhoff, A.: Theory of Wing Sections, McGraw-Hill Book Company, Inc., 1949
- Hoerner, S.F.: Fluid Dynamic Drag, Hoerner Fluid Dynamics, 1951/1965
- Hoerner, S.F.: Fluid Dynamic Lift, Hoerner Fluid Dynamics, 1975
- Perkins, C.D. and Hage, R.E.: Airplane Performance, Stability and Control, John Wiley ans Sons, 1949

Vehicle Aerodynamics
- Hucho, Wolf-Heinrich: Aerodynamics of Road Vehicles, SAE International, 1998
- Gillespi, Thomas D: Fundamentals of Vehicle Dynamics, SAE, 1992
- Katz Joseph: New Directions in Race Car Aerodynamics, Robert Bentley Publishers, 1995
227-0124-00LEmbedded Systems Information Restricted registration - show details W6 credits4GL. Thiele, M. Magno
AbstractAn embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions.
ObjectiveUnderstanding specific requirements and problems arising in embedded system applications.

Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis.

Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system FreeRTOS, a commercial embedded system platform and the associated design environment.
ContentAn embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices.

The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides, the lecture is complemented by laboratory sessions where students learn to program in C, to base their design on the embedded operating systems FreeRTOS, to use a commercial embedded system platform including sensors, and to edit/debug via an integrated development environment.

Specifically the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, hardware architecture synthesis.

More information is available at Link .
Lecture notesThe following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at Link .
LiteratureP. Marwedel: Embedded System Design, Springer, ISBN 978-3-319-56045-8, 2018.

G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, ISBN 978-1-4614-0676-1, 2011.

Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press, ISBN 978-0-262-53381-2, 2017.

M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 978-0-128-05387-4, 2016.
Prerequisites / NoticePrerequisites: Basic knowledge in computer architectures and programming.
227-0225-00LLinear System TheoryW6 credits5GA. Iannelli
AbstractThe class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
ObjectiveStudents should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content- Proof techniques and practices.
- Linear spaces, normed linear spaces and Hilbert spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, duality. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
Lecture notesAvailable on the course Moodle platform.
Prerequisites / NoticeSufficient mathematical maturity, in particular in linear algebra, analysis.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
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