Syllabus Book - uni-due.de...Learning objectives The students are able to compute potential...

Syllabus Book

Master Automation and Control Engineering PO08 Version: 04.07.2018

Description of the degree course

Name of the degree course Shorthand expression of degree course Master Automation and Control Engineering PO08 M-ACE_PO08 Type Period of study SWS ECTS-Credits

Master 4 66 120 Description In principle the master study program continues the qualification of understanding the context of complex technical systems from the bachelor study program, but now on a higher scientific level and with the opportunity for the students of setting up priorities in their degree. Hereby, the graduates become suitable for careers as engineers in several occupation areas, e.g. in the area of conception, planning and project development of automation in the branches of process control, manufacturing control, electrical power control, central building control, traffic control or control within cars or aeroplanes. Furthermore there exists a particular suitability to the solving of particularly complex or demanding tasks. Therefore the following additional occupations come into play for the Master’s graduates: • Research, • Project management, • Management positions with personnel responsibilities. Based on broad basic knowledge from the Bachelor study, in-depth knowledge in the following fields will be provided: • Mathematics (among others vector analysis, numerical mathematics) and additional physical basics (fluid dynamics), • Automation technology (with in-depth theoretical methods), • Computer science (real-time systems and distributed computer systems). With this the following special goals are aimed at: • Principle ability to familiarise with theoretical complex themes, • Ability to solve complex automation tasks, which require demanding methods of modelling, simulation and control.

Study plan

V Ü P S Cr

Master Automation and Control Engineering PO08 Elektrotechnik und Informationstechnik

37 19 4 6 120

1. Mathematics E4 Prof. Dr. Scheven d 2 1 0 0 5

Nonlinear Control Systems Prof. Dr.-Ing. Ding e 2 1 0 0 4

Nonlinear Control Systems Lab Prof. Dr.-Ing. Ding e 0 0 1 0 1

Numerical Mathematics Prof. Dr. Scheven e 2 2 0 0 6

Test and Reliability of Digital Systems Prof. Dr.-Ing. Hunger e 2 1 0 0 4

Theory of Statistical Signals Prof. Dr.-Ing. Czylwik d 2 2 0 0 5

Elective 1 NN d/e 2 1 0 0 4

Total 12 8 1 0 29

2. Algorithmic Numerics Dr.-Ing. Petersen d 3 1 0 0 6

Fault Diagnosis and Fault Tolerance in Technical Systems Prof. Dr.-Ing. Ding d 2 1 0 0 4

Cognitive Technical Systems Prof. Dr.-Ing. Söffker d 2 1 0 0 4

Real-Time Systems Prof. Dr. rer. nat. Pauli e 3 1 0 0 5

Advanced Control Lab Prof. Dr.-Ing. Ding d 0 0 3 0 4

State and Parameter Estimation Prof. Dr.-Ing. Ding e 2 1 0 0 4


Total 14 6 3 0 31

3. Advanced System and Control Theory Prof. Dr.-Ing. Ding e 2 1 0 0 4

Distributed Systems Prof. Dr.-Ing. Weis e 3 1 0 0 6

Non-technical Catalog M NN d/e 0 0 0 6 8

Robust Control Prof. Dr.-Ing. Ding e 2 1 0 0 4

Fluid Flow 2 Prof. Dr.-Ing. Kempf d 2 1 0 0 4


Total 11 5 0 6 30

4. Master Thesis NN d/e 0 0 0 0 27

Master Thesis Colloquium NN d/e 0 0 0 0 3

Total 0 0 0 0 30

Module- und course catalog

Module Name Vector Analysis and Advanced Numerics Module Coordinator Prof. Dr.-Ing. Uwe Maier Used in degree course • Master Automation and Control Engineering PO08

Year Duration Type of module 1 2 Pflichtmodul

Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Numerical Mathematics 1 4 180 6 2 Mathematics E4 1 3 150 5 3 Algorithmic Numerics 2 4 180 6 Total 11 510 17

Module Name Vector Analysis and Advanced Numerics Course/Examination Name Mathematics E4 Course Coordinator Prof. Dr. Christoph Scheven

Semester Cycle Language 1 WS deutsch

SWS Contact hours Self-study hours Workload in h ECTS-Credits 3 45 105 150 5

Teaching form Lecture with Exercises Learning objectives The students are able to compute potential functions of conservative vector fields. They know how to parametrize important surfaces. They are also able to calculate surface- and flow integrals and in so doing apply integral theorems. They know what a boundary value problem is and are capable of solving such problems for simple cases. Description The course deals with the following subjects: Vector analysis - Potential functions and line integrals - Integration in several variables - Parameterized surfaces - Surface integrals - Flow integrals - Green’s theorem - Stoke’s theorem - Gauss’s theorem Partial differential equations - Introduction - Green’s identities - Poisson’s integration equations over a circular disk and a sphere - fundamentals of Distributions Kind of examination Written examination 120 min Literature Burg, Haf, Wille: Mathematik für Ingenieure, I-IV,2002; Marsden, Tromba: Vectoranalysis,1996; Kevorkian: Partial Differential Equations,2000; Renardy/Rogers: A first graduate course in Partial Differential Equations,2004; Evans: Partial Differential Equations, 2010. Requirements Mathematik 1 für Ingenieure und Mathematik 2 für Ingenieure.

Module Name Vector Analysis and Advanced Numerics Course/Examination Name Numerical Mathematics Course Coordinator Prof. Dr. Christoph Scheven

Semester Cycle Language 1 WS englisch


Teaching form Lecture / Exercise Learning objectives The students should learn, to solve typical problems in engineering-mathematics by numerical methods, among others: Linear and nonlinear systems, eigenvalues, interpolation, differential equations and integration. They should learn to implement general methods into a practical computation and to evaluate them with respect to accuracy and efficiency. Description The course deals with the following subjects: 1 Error Analysis Representation of numbers, Floating-point-numbers, Rounding errors, Error Propagation, Error propagation in arithmetic operations, Condition numbers 2 Nonlinear equations The method of Bisection, The secant method, Newton‘s method, Fixed point iteration, Polynomial equations, Systems of nonlinear equations, Newton‘s method for systems 3 Systems of Linear Equations The LR and Cholesky Decomposition, The LR-Decomposition, The Cholesky Decomposition, Gauss Elimination and Back-Substitution, Pivoting strategies, The QR Decomposition, Data fitting; Least square problems, lterative solutions, Jacobi Iteration (total-step-method), Gauss-Seidel-Iteration (single-step-method), Convergence properties 4 Finding Eigenvalues The Power method, Localizing eigenvalues, The QR-method, Hessenberg matrices 5 Ordinary Differential Equations Basic analytic methods, Separation of variables, Linear differential equations, One-step-methods, Euler‘s Method, Midpoint Euler, Two-stage-models, Runge-Kutta-methods 6 Polynomial Interpolation Lagrange form of Interpolation Polynomial, Interpolation Error, Divided Differences, Spline Interpolation 7 Numerical Integration Gaussian Quadrature Kind of examination written exam 120 min.

Literature ·1 Gautschi, W. Numerical Analysis, Birkhäuser,1997. ·2 Hammerlin und Hoffmann. Numerische Mathematik, Springer,1994. ·3 Householder. A.S. Principles of Numerical Analysis, Dover Publications,1974. ·4 Kincaid,D. and Cheney, W. Numerical Analysis, Brooks/Cole Publishing,1991. ·5 Locher. Numerische Mathematik für Informatiker,1993. ·6 Philipps,C. and Cornelius, B. Computional Numerical Methods, Ellis Hoorwood. ·7 Stoer, J. and Burlisch, R. Introduction to numerical Analysis,2005. Requirements Mathematik 1 für Ingenieure und Mathematik 2 für Ingenieure

Module Name Vector Analysis and Advanced Numerics Course/Examination Name Algorithmic Numerics Course Coordinator Dr.-Ing. Dipl.-Inform. Jörg Petersen

Semester Cycle Language 2 SS deutsch


Teaching form Presence lecture with service of e-learning platform Moodle. Learning objectives The students are able to implement numerical methods as algorithms. They can transfer these into Matlab in a numerically efficient way, can solve practically relevant tasks on their own and can visualise results. They are able to teach themselves additional and more specific methods as well as implement them by Matlab or similar tools. Description After an introductory overview the algorithmic implementation of numerical methods will be focused on. Following topics are included: - coding of numbers in computers: unsigned and signed integers, floating point numbers by IEEE 754 standard; - error analysis, error propagation, condition, stability; - algebraic equations, simple and complete Horner scheme, roots of polynoms; - non-linear equations, searching for roots of continuous functions, bisection method, fixed point iteration, Newton and secant method; - direct and iterative methods solving linear equation systems: Gaussian, Jacobi, Gauss Seidel method, relaxation method; - discrete and continuous approximation, approximation of periodical functions, discrete fast Fourier transform; - interpolation polynoms, kubic spline interpolation, Bézier polynoms, Bézier splines; - integration, Newton-Cotes Formulas, Gaussian Quadrature Numerical Integration, Monte Carlo integration; - numerical differentiation with differential quotients and substitution functions; - ordinary differential equations with starting and boundary value problems, one and multi step methods, systems of ordinary differential equations, stability and stiffness, shooting method, finite difference equations; - partial differential equations of second order with starting and boundary value problems, finite difference equations; - matrix Eigen values and vectors, power method, QR algorithm; - linear optimisation, simplex method, transport problems. In the exercises algorithms are developed which are implemented in Matlab/Octave. Kind of examination written examination 90 min.

Literature - Chapra, Steven C. Applied Numerical Methods W/MATLAB: for Engineers & Scientists. McGraw-Hill Verlag. 3rd edition. 2011. ISBN 978-0073401102. - Chapra, Steven C.; Canale, Raymond P. Numerical Methods for Engineers. 6th edition. McGraw-Hill Verlag. 2009. ISBN 978-0073401065. - Engeln-Müllges, G.; Niederdrenk, K. Numerik-Algorithmen: Verfahren, Beispiele, Anwendungen. 10. Auflage. Springer Verlag. 2011. ISBN 978-3642134722. - Gramlich, G.; Werner, W. Numerische Mathematik mit Matlab. dpunkt-Verlag. 2000. ISBN 3-932588-55-X. - Weller, F. Numerische Mathematik für Ingenieure und Naturwissenschaftler. Eine Einführung für Studium und Praxis. Vieweg Verlag. 2. Auflage 2001. ISBN 3-528-03818-7. - Yang, Won Y.; Cao, Wenwu; Chung, Tae-Sang; Morris, John. Applied Numerical Methods Using MATLAB. John Wiley & Sons. 2005. ISBN 0-471-69833-4. - http://de.mathworks.com/products/matlab/ Requirements Grundlagen der Linearen Algebra (Vektor-, Matrixrechnung, Determinanten), Grundlagen der Analysis (Reihen, stetige Funktionen, Differentiation, Integration, Taylorentwicklung), grundlegende Programmierkenntnisse in C/C++ oder Java oder Matlab.

Module Name Stochastic Methods in Automation Module Coordinator Prof. Dr.-Ing. Uwe Maier Prof. Dr.-Ing. Andreas Czylwik Used in degree course • Master Elektrotechnik und Informationstechnik (Automatisierungstechnik) PO06 • Master Automation and Control Engineering PO08


Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Theory of Statistical Signals 1 4 150 5 2 State and Parameter Estimation 2 3 120 4 Total 7 270 9

Module Name Stochastic Methods in Automation Course/Examination Name Theory of Statistical Signals Course Coordinator Prof. Dr.-Ing. Andreas Czylwik



Teaching form Lecture and exercise Learning objectives A lot of processes (from physics, economics, biology, technology …) cannot be described only with deterministic relationships, but need statistical methods. Students who have completed this course should be able to apply the concepts from stochastic variables and stochastic processes in practical problems. Description After a sound introduction in the notion of probability, stochastic variables will be discussed in detail. To that belong the different description possibilities through probability density function, probability distribution function and characteristic function. Beyond that, the properties of functions from stochastic variables will be handled. Stochastic processes which are extended from stochastic variables in time dimension will be emphasized on. Second-order moments such as the autocorrelation function, the cross correlation function as well as the corresponding power spectral density will be particularly discussed. Special stochastic processes of great practical importance such as the Gauss’s and Poisson’s processes will be handled. In conclusion, applications like optimal filters and modulation will be discussed. The contents will be deepened in exercises. Kind of examination Written examination (90 min) Literature A. Papoulis: Probability, random variables and stochastic processes, McGraw-Hill, 2. Aufl. 1984

Module Name Stochastic Methods in Automation Course/Examination Name State and Parameter Estimation Course Coordinator Prof. Dr.-Ing. Steven X. Ding

Semester Cycle Language 2 SS englisch


Teaching form Lecture and exercises Learning objectives The students should learn basic state estimation and parameter identification methods and be able to implement them in form of algorithms. Description A dynamic system is well described by its model structure, state variables and model parameters. In practice, they are often unknown and should be identified or estimated. In this course, basic methods for the identification and estimation of state variables and system parameters are introduced. The course consists of four thematic blocks. In Block I, State estimation - Kalman filter and observer schemes, different types of Kalman filters and observer schemes are introduced on the assumption that the system model and parameters are available, including • state estimation in static processes • State estimation in (linear) dynamic processes • H2 optimal observer. In Block II, Parameter identification - Least squares parameter estimation schemes, parameter identification is dealt on the assumption of a given system structure. Topics like parameter estimation in static processes, parameter estimation in dynamic processes and recursive algorithms are addressed. In case that the system is a block box, system identification is needed. In Block III, System identification - Subspace identification methods (SIM), the basic ideas and procedure of SIM are first introduced. It is followed by some standard SIMs. Block IV, SIM-added identification of kernel and image representations and data-driven design of feedback controllers and observers, is dedicated to the introduction of some data-driven design methods for controllers and observers. Kind of examination Written examination with a duration of 90 min. Language: English. Literature [1] S. X. Ding, Vorlesungsskript "State and parameter estimation” (wird jährlich aktualisiert, per Download verfügbar, will be updated and available for download) [2] T. Kailath and A. Sayed and B. Hassisi, Linear estimation, Prentice Hall, 1999. [3] R. Isermann and M. Münchhof, Identification of Dynamic Systems Springer-Verlag, 2011 [4] B. Huang and R. Kadali, Dynamic Modeling, Predictive Control and Performance Monitoring - A Data-driven Subspace Approach. Springer-Verlag, London 2008 [5] S. X. Ding, Data-driven design of fault diagnosis and fault-tolerant control systems, Springer-Verlag, 2014.

Module Name Advanced Control 1 Module Coordinator Prof. Dr.-Ing. Steven X. Ding Used in degree course • Master Automation and Control Engineering PO08


Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Nonlinear Control Systems 1 3 120 4 2 Nonlinear Control Systems Lab 1 1 30 1 3 Advanced Control Lab 2 3 120 4 Total 7 270 9

Module Name Advanced Control 1 Course/Examination Name Nonlinear Control Systems Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Lecture /Exercise Learning objectives The students should be able to model nonlinear control systems, to analyze the system dynamic behavior, in particular the stability using different methods, and to design nonlinear control systems for applications. Description During the last two decades, development of advanced nonlinear control system theory has received much attention. This course is devoted to the essentials of the nonlinear system analysis and to the introduction of some advanced methods of analyzing and designing nonlinear control systems developed in recent years. First, different methods and tools for the description of nonlinear systems are introduced. Stability study with emphasis on the Lyapunov methods builds the basis for the further study. It is followed by the study on passive and disspative systems, and presentation of different methods of nonlinear controller design including the feedback linearization, sliding control, adaptive control schemes and nonlinear observer design. Kind of examination written exam 90 min. Literature [1] S. X. Ding, Vorlesungsskript "Nonlinear control systems" (wird jährlich aktualisiert, per Download verfügbar, will be updated and available for download) [2] H. K. Khalil: Nonlinear systems, the 3rd edition, Prentice Hall, 2002. [3] J.-J. E. Slotine and W. Li, Applied nonlinear control, Prentice Hall, 1991

Module Name Advanced Control 1 Course/Examination Name Nonlinear Control Systems Lab Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Laboratory Learning objectives The students will be able to model, analyze the nonlinear control systems being available in the lab and to design satisfactory nonlinear control systems. Description The students will learn how to develop a control scheme for nonlinear processes and how to realize the developed controller on-line under real application conditions. For this purpose, different laboratory systems with real plants and design software (MATLAB) are available. Kind of examination test, experimental procedure Literature Introduction to the lab. / Versuchsanleitung

Module Name Advanced Control 1 Course/Examination Name Advanced Control Lab Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Laboratory Learning objectives The students are able to model and analyze different laboratory systems and to develop suitable control schemes. Description The students learn how to develop a control scheme for a given process and how to realize the developed controller on-line under real application conditions. For this purpose, different laboratory systems with real plants and design software (MATLAB) are available. Kind of examination test, experimental procedure Literature AKS internal document: Instruction to Advanced Control Lab Requirements Control Engineering E, Modern Control Systems (RTE, ZREG)

Module Name Advanced Control 2 Module Coordinator Prof. Dr.-Ing. Steven X. Ding Used in degree course • Master Automation and Control Engineering PO08

Year Duration Type of module 1+2 2 Pflichtmodul

Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Robust Control 3 3 120 4 2 Advanced System and Control Theory 3 3 120 4 Total 6 240 8

Module Name Advanced Control 2 Course/Examination Name Advanced System and Control Theory Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Lecture, Exercise Learning objectives The students should be able to model different types of networked control systems. Moreover, they should be able to apply optimal control schemes to real discrete-time systems. Description This course is devoted to the analysis and synthesis of discrete-time, sampled-data, multi-rate sampled data and networked control systems. It consists of four parts. Part I: Introduction and basics. In this part, basic concepts for discrete control systems are reviewed, including state feedback controllers, observer-based state feedback controllers, stability check and decoupling controller design. Part II: Optimal control schemes. In this part, four optimal control schemes are introduced: - Model predictive control (MPC) - linear quadratic regulator (LQR) - Dynamic programming - Calculus of variations and optimal control Part III: Networked control systems. In this part, Multi-rate discrete-time systems, different types of networked control systems (NCS) are addressed. The focus is on the control-oriented modelling technique like lifting methods. Part IV: LMI-aided system analysis and synthesis. In this part, design of H_2 and H_inf controllers for discrete-time systems with unknown inputs and model uncertainties is presented. To this end, LMI (linear matrix inequality) technique is applied. Kind of examination written examination of 90 min. Literature [1] S. X. Ding, Vorlesungsskript "Advanced system and control theory" (wird jährlich aktualisiert, per Download verfügbar, will be updated and available for download) [2] K. Zhou et al., Robust and Optimal Control, Prentice Hall, 1996. [3] E.F. Camacho and C. Bordons, Model predictive control, Springer, 1999 [4] F.L. Lewis, D. Vrabie, L. Vassilis, Optimal Control (3rd Edition) John Wiley & Sons, 2012 Requirements Essentials of control engineering

Module Name Advanced Control 2 Course/Examination Name Robust Control Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Lecture / Exercise Learning objectives The students will be able to model and analyze uncertain control systems and to design different robust controllers. Description Due to its importance in practice, robust control technique is one of the research and development fields in control engineering, which continuously received the most attention during the last two decades. The focus of this course is the introduction to the essentials of the robust control theory, to the computational tools and some design methods. The course consists of four parts. In Part 1, Introduction, the system configurations and internal stability of feedback loops are addressed. Part II, Control system configurations, parameterizations, and tools, is dedicated to parameterizations of stabilization controllers as well as observers and their configurations. The major mathematical tool is the factorization technique. In Part III, System analysis, controller design and design tools, standard robust control schemes, the so-called H_2 and H_inf control schemes as well as the associated mathematical knowledge are introduced. Moreover, the LMI (linear matrix inequality) technique for the system analysis and design is presented. Part IV, Robust controller design for uncertain systems, deals with systems with model uncertainties. Some basic schemes are introduced. Kind of examination written examination with a duration of 90 minutes, language: English Literature [1] S. X. Ding, Vorlesungsskript "Robust control" (wird jährlich aktualisiert, per Download verfügbar, will be updated and available for download) [2] K. Zhou, Essentials of robust control, Prentice Hall, 1998 [3] S. X. Ding, Data-driven design of fault diagnosis and fault-tolerant control systems, Springer-Verlag, 2014

Module Name Advanced Automation Module Coordinator Prof. Dr.-Ing. Uwe Maier Used in degree course • Master Automation and Control Engineering PO08


Nr. Courses/Exams Semester SWS Workload in h

ECTS-Credits

1 Fault Diagnosis and Fault Tolerance in Technical Systems 2 3 120 4

2 Cognitive Technical Systems 2 3 120 4 Total 6 240 8

Module Name Advanced Automation Course/Examination Name Fault Diagnosis and Fault Tolerance in Technical Systems Course Coordinator Prof. Dr.-Ing. Steven X. Ding



Teaching form Lecture/Exercise Learning objectives The students should be able to apply statistical, data-driven and model-based FDI and FTC methods to real cases. Description A very critical and important issue concerning the design of automatic control systems with increasing complexity is to guarantee a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solution, advanced fault detection and identification (FDI) technology and fault tolerant systems (FTC) are receiving considerable attention. The objective of this course is to introduce basic model based FDI and fault tolerant schemes, advanced analysis and design algorithms and the needed tools. The course consists of 6 parts. Part I: Basic fault detection problems and the associated solutions. The following two topics are addressed in this part: • Basic statistical methods for change/fault detection • Basic deterministic methods for change/fault detection Part II: Basic data-driven methods The following two topics are addressed: • Basic data-driven methods for statistic processes • A basic data-driven method for deterministic processes Part III: model-based FDI methods • Two essential problems • Essentials: Modelling and residual generation • Fault detection in stochastic systems • Fault detection in deterministic systems Part IV: Data-driven design of dynamic FDI systems • Subspace identification technique (SIT) aided design of observer-based FDI systems Part V: Fault isolation and identification schemes • Basic isolation and identification methods • Methods to a structural fault isolation (for dynamic processes) Part VI: Fault-tolerant systems Kind of examination written examination (90 min). Literature Steven X. Ding, Model-based fault diagnosis techniques, Springer-Verlag, 2008.

Module Name Advanced Automation Course/Examination Name Cognitive Technical Systems Course Coordinator Prof. Dr.-Ing. Dirk Söffker



Teaching form The lecture notes will be posted on the department pages for participants in the event. The lecture will be given by means of a tablet PC. The event of the underlying publications as background material in the form of PDF documents available online. Learning objectives Automation technology - due to their interdisciplinary, systems-oriented approach - is an interdisciplinary engineering discipline. The aim of the lecture Cognitive Technical Systems, is to familiarize the students with the basics of modern computer science, with filtering methods, with methods of artificial intelligence and cognitive technical systems, enabling them to recognize the development of control and automation technology with the means of cognitive artificial intelligence in the sense of an expansion, and to master and use the underlying methods. Description - introduction - motivation - Task fields basics - principle - agents - Behavior coordination (with agents) - behavioral description - Modelling human interaction - cognitive architectures - knowledge Representation - Planning, action, Search - learning Tools I: Filtering Tools II: Classification and Learning Current research applications of the Department of SRS the workspace Cognitive Technical Systems: - Situations operator modeling - Stabilization of nonlinear dynamic systems without model knowledge - Personalized, adaptive and interactive driver Assistance - Planning and assistance systems in aviation - Adaptive mobile robotics

Kind of examination Written exam (90 min.). Exceptions to that (oral exam e.g. because of small attendance) will be announced at the beginning of the term. Literature Alpaydin, E.: Maschinelles Lernen, Oldenbourg, 2008. (idt.: Machine Learning, MIT Press, 2003). Cacciabue, P.C.: Modelling and Simulation of Human Behaviour in System Control, Springer, 1998. Ertel, W.: Grundkurs der Künstlichen Intelligenz, Vieweg, 2008. Görz, G. et al.: Handbuch der Künstlichen Intelligenz, Oldenbourg, 2003. Haykin, S.: Neural Networks and Learning Machines, Pearson, 2009. Johannsen, G.: Mensch-Maschine-Systeme, Springer, 1993. Russel, S.; Norvig, P.: Künstliche Intelligenz, Pearson, 2004. (idt.: Artificial Intelligence, Prentice Hall, 2003).

Module Name Fluid Dynamics Module Coordinator Prof. Dr.-Ing. Andreas Markus Kempf Used in degree course • Master Elektrotechnik und Informationstechnik (Automatisierungstechnik) PO06 • Master Automation and Control Engineering PO08 • Master NanoEngineering (Nanoprozesstechnologie) PO12 • Master Elektrotechnik und Informationstechnik (Automatisierungstechnik) PO12 • Master Automation and Control Engineering PO15


Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Fluid Flow 2 3 3 120 4 Total 3 120 4

Module Name Fluid Dynamics Course/Examination Name Fluid Flow 2 Course Coordinator Prof. Dr.-Ing. Andreas Markus Kempf



Teaching form Electronic presentation with slides, supplementary hand written annotation. Lecture notes are provided for download (Moodle). The lecture is accompanied by a tutorial seminar. Learning objectives Students which attended the lecture are capable: 1. To analyze complex fluid mechanical problems and to find an adequate mathematical description 2. To classify fluid mechanical problems and to apply simplifying model assumptions 3. To solve fluid mechanical problems using the potential theory 4. To correctly estimate viscous effects and to apply suitable rheological models 5. To recognize the effects of compressibility and to find mathematical description for one-dimensional flows; To calculate heat and pressure losses in viscous, compressible flows Description The lecture teaches the continuum mechanical models of fluid mechanics, their basics and simplifying assumptions. Main topics are: 1. Kinematics of fluids and transport theorem 2. Konservation equations for mass, momentum and energy 3. Similarity of flows 4. Viscous, incompressible flows 5. Creeping flow 6. Potential flow theory 7. Boundary layer theory and introduction to turbulent flows 8. One-dimensional stream tube theory of compressible flows Kind of examination Written examination, 120 min. Literature - Script - Moodle

Module Name Computer Engineering for Automation Module Coordinator Prof. Dr.-Ing. Uwe Maier Used in degree course • Master Automation and Control Engineering PO08


Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Test and Reliability of Digital Systems 1 3 120 4 2 Real-Time Systems 2 4 150 5 3 Distributed Systems 3 4 180 6 Total 11 450 15

Module Name Computer Engineering for Automation Course/Examination Name Test and Reliability of Digital Systems Course Coordinator Prof. Dr.-Ing. Axel Hunger



Teaching form Lecture and exercises Learning objectives The students are able to qualitatively and quantitatively evaluate and rate the reliability of digital systems (hardware, software, and network). In addition, they are able to assess the relations between physical errors, test, simulation and design for testability and to select best approach for a given application with good reasons. Description Within this lecture, the characteristics of technical systems are analyzed and measured concerning their hazard potential. Furthermore, measures are presented, with which the quality of technical systems in the sense of an increased life span or a safe behavior can be achieved also in the case of an error. After completion of the lecture, students are familiar with the fundamentals for the description of erroneous technical systems and they are able to select an appropriate method, which promises the best results under economically justifiable expenditure for a given task. Besides that, the students are able to describe the error behavior of technical systems on different levels. They can distinguish between best use of the traditional methods to determine lift time, the use of redundancy to increase lifetime, Markov-chains and practical tools like FMEA and FMECA. They shall also be able to describe and handle the faulty behavior of technical systems on different levels. Systems discussed are complex mechatronic systems like cars and airplanes as well as electrical circuits and systems. In the area of testing, the test of digital circuits and systems is considered as well as computer systems and the software running on them. In this context, they can distinguish between different fault models, appropriate for different systems, simulation and test generation as well as Design for Testability. Kind of examination Written examination with a duration of 90 minutes. Literature 1. M. Lazzaroni et al. (2012) Reliability Engineering - Basic Concepts and Applications in ICT, Springer. 2. A. Birolini (2010) Reliability Engineering - Theory and Practice, Springer. 3. A. Miczo (2003) - Digital Logic Testing and Simulation, Wiley. 4. A. Meyna and B. Pauli (2003) - Taschenbuch der Zuverlässigkeits- und Sicherheitstechnik, Hanser. 5. H.-D. Kochs (1994) - Zuverlässigkeit großer und komplexer Systeme, Institut für Informatik, Duisburg. 6. H. Wojtkowiak (1988) - Test und Testbarkeit digitaler Schaltungen, Teubner.

Module Name Computer Engineering for Automation Course/Examination Name Real-Time Systems Course Coordinator Prof. Dr. rer. nat. Josef Pauli

Semester Cycle Language 2 SS englisch


Teaching form Lecture course (in-class lecture, with Powerpoint) and computer-based exercises (in-class lecture, with Powerpoint and Blackboard) Learning objectives Getting basic knowledge and understanding of real-time systems. Mapping real-time tasks into computer-based solutions by using real-time modelling tools, operating systems, and high-level languages. Judging various high-level languages for implementing certain real-time applications. Description Real-time systems have to process the data within specified timing constraints, which is an inherent characteristic of the correctness of a programm (apart from semantical correctness). The course treats the development of real-time systems using high-level languages (Ada, C++, Java). Included is the modelling of time and real-time behaviors, scheduling, concurrency, synchronisation, communication, time and event controlled systems. Overview: - Introduction - Real-time entities, abstractions, errors - Programming using high-level languages - Concurrency in high-level languages - Synchronisation and communication - Atomic actions and concurrent processes - Real-time facilities in computer systems - Resource control and scheduling - Real-time systems in automation application Kind of examination Written examination (SS, 90 Min.) resp. oral examination (WS, 30 Min.) Literature - A. Burns, et al.: Real-Time Systems and Programming Languages, Addison-Wesley, 2001. - J. Benra und W. Halang: Software-Entwicklung für Echtzeitsysteme; Springer, 2009. - G. Buttazzo: Hard Real-time Computing Systems, Springer, 2011. - H. Kopez: Real-time Systems - Design Principles for Distributed Embedded Applications; Kluwer, 2011. - Q. Li, C. Yao: Real-Time Concepts for Embedded Systems. CMP Books, 2003. - J. Liu: Real-Time Systems, Prentice-Hall, 2000. - A. Shaw: Real-time Systems and Software, John Wiley, 2001. - Wilhelm et al. The Worst-Case Execution Time Problem - Overview of Methods and Survey of Tools. ACM TEXS, 2008.

Requirements Grundkenntnisse in Java oder C/C++, Grundkenntnisse in Betriebssysteme

Module Name Computer Engineering for Automation Course/Examination Name Distributed Systems Course Coordinator Prof. Dr.-Ing. Torben Weis



Teaching form Lecture (3 SWS), Exercise (1 SWS) Learning objectives The students know the principles, protocols, algorithms and architecture of distributed systems are able to apply these to real word problems. Description The lecture presents important concepts and protocols for distributed systems. The lecture starts with principles of distributed communication: - Data serialization (ASN.1, CORBA XDR, SOAP) - Remote procedure calls - Distributed objects The sencond part of the lecture presents important and often used distributed algorithms: - Physical clocks - Logical clocks - Transactions - Synchronisation - Replication and consistency - Global state Kind of examination Written exam (90 min.) Literature ·1 Coulouris/Dollimore/Kindberg: Distributed Systems - Concepts and Design, Addison-Wesley 2001 (3rd edition). ·2 Tannenbaum/van Steen: Distributed Systems - Principles and Paradigms, Prentice Hall 2002. ·3 Borghoff/Schlichter: Rechnergestützte Gruppenarbeit (in German), Springer 1998.

Module Name Electives Module Module Coordinator NN Used in degree course • Master Computer Engineering PO04 • Master Computer Science and Communications Engineering PO04 • Master Control and Information Systems PO04 • Master Electrical and Electronic Engineering (Communications Engineering) PO04 • Master Electrical and Electronic Engineering (Power and Automation) PO04 • Master Mechanical Engineering (Water Resources and Environmental Engineering) PO04 • Master Mechanical Engineering (Production and Logistics) PO04 • Master Mechanical Engineering (Mechatronics) PO04 • Master Mechanical Engineering (General Mechanical Engineering) PO04 • Master Management and Technology of Water and Waste Water PO08 • Master Automation and Control Engineering PO08 • Master Electrical and Electronic Engineering (Communications Engineering) PO08 • Master Electrical and Electronic Engineering (Power and Automation) PO08 • Master Computer Engineering (Reliable Systems) PO08 • Master Computer Engineering (Interactive Systems and Visualization) PO08 • Master Computer Science and Communications Engineering PO08 • Master Mechanical Engineering (Energy and Environmental Engineering) PO08 • Master Metallurgy and Metal Forming PO08 • Master Mechanical Engineering (General Mechanical Engineering) PO08 • Master Mechanical Engineering (Mechatronics) PO08 • Master Mechanical Engineering (Production and Logistics) PO08

Year Duration Type of module 1+2 3 Wahlpflichtmodul

Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Elective 1 1 3 120 4 2 Elective 2 2 3 120 4 3 Elective 3 3 3 120 4 Total 9 360 12

Module Name Electives Module Course/Examination Name Elective 1 Course Coordinator NN

Semester Cycle Language 1 deutsch/englisch


Teaching form Learning objectives With a targeted choice of the elective subjects, the students should follow their affinities and qualify themselves for a job resp. for an academic career. Description The electives module should give the students the opportunity to expand the focus of their study program and of their specialization. By so doing, the deepness of the disciplinary education becomes more important. This can be on one hand very precious for a clearly defined professional use but on the other hand a door-opening to a scientific research (PhD) consecutive to the master degree. Alternatively, other subjects, which are relevant of other study fields of the Faculty of Engineering or which belong to other specializations, could also be chosen. In this way, interdisciplinary abilities, which are considerably important in the professional world in the sense of double qualifications, could be acquired. Kind of examination According to the examination regulation the type and duration of the examination will be defined from the lecturer before the semester starts. Literature

Module Name Non-technical Subjects M Module Coordinator NN Used in degree course • Master Computational Mechanics PO07 • Master Management and Technology of Water and Waste Water PO08 • Master Automation and Control Engineering PO08 • Master Electrical and Electronic Engineering (Communications Engineering) PO08 • Master Electrical and Electronic Engineering (Power and Automation) PO08 • Master Computer Science and Communications Engineering PO08 • Master Mechanical Engineering (Energy and Environmental Engineering) PO08 • Master Metallurgy and Metal Forming PO08 • Master Mechanical Engineering (General Mechanical Engineering) PO08 • Master Mechanical Engineering (Mechatronics) PO08 • Master Mechanical Engineering (Production and Logistics) PO08 • Master Automation and Control Engineering PO15 • Master Communications Engineering PO15 • Master Power Engineering PO15 • Master Computer Engineering (Interactive Systems and Visualization) PO15 • Master Computer Engineering (Intelligent Networked Systems) PO15 • Master Embedded Systems Engineering PO15 • Master Management and Technology of Water and Waste Water PO15 • Master Metallurgy and Metal Forming PO15 • Master Mechanical Engineering (General Mechanical Engineering) PO15 • Master Mechanical Engineering (Mechatronics) PO15 • Master Mechanical Engineering (Production and Logistics) PO15 • Master Mechanical Engineering (Energy and Environmental Engineering) PO15 • Master Computational Mechanics PO15 • Master Mechanical Engineering (Ship and Offshore Technology) PO15

Year Duration Type of module 2 1 Wahlmodul

Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Non-technical Catalog M 3 0 240 8 Total 6 240 8

Module Name Non-technical Subjects M Course/Examination Name Non-technical Catalog M Course Coordinator NN

Semester Cycle Language 3 WS+SS deutsch/englisch


Teaching form The type of instruction depends on the chosen course. Learning objectives The module aims at deepening the general knowledge of the students and resp. at improving their language skills as well as strengthening their professional qualifications through the learning of teamwork and expose techniques. Description This module offers the students the opportunity to, besides the pure technical courses they take, attend some so called “non-technical subjects” and latter provide an attest for them. These courses can be chosen from the overall offers of the Duisburg-Essen university, whereby the “Institut für Optionale Studien“(IOS) proposes a catalog containing courses which fall under the named supplementary area. Kind of examination The type and duration of the examination will be defined from the lecturer before the semester starts. Literature Spezifisch für das gewählte Thema

Module Name Master-Thesis Module Coordinator NN Used in degree course • Master Computational Mechanics PO07 • Master Management and Technology of Water and Waste Water PO08 • Master Automation and Control Engineering PO08 • Master Electrical and Electronic Engineering (Communications Engineering) PO08 • Master Electrical and Electronic Engineering (Power and Automation) PO08 • Master Computer Engineering (Reliable Systems) PO08 • Master Computer Engineering (Interactive Systems and Visualization) PO08 • Master Computer Science and Communications Engineering PO08 • Master Mechanical Engineering (Energy and Environmental Engineering) PO08 • Master Metallurgy and Metal Forming PO08 • Master Mechanical Engineering (General Mechanical Engineering) PO08 • Master Mechanical Engineering (Mechatronics) PO08 • Master Mechanical Engineering (Production and Logistics) PO08

Year Duration Type of module 2 1 Wahlpflichtmodul

Nr. Courses/Exams Semester SWS Workload in h ECTS-Credits 1 Master Thesis 4 0 0 27 2 Master Thesis Colloquium 4 0 0 3 Total 0 0 30

Module Name Master-Thesis Course/Examination Name Master Thesis Course Coordinator NN



Teaching form Master Thesis 6 month including a colloquium Learning objectives The master thesis is used to show that a student is capable of processing a problem from the corresponding field of engineering sciences autonomously and with scientific methods and presenting it comprehensibly, within a given period of time. Description The master thesis is an examination paper which concludes the scientific education in every master degree course within the academic program ISE. Within the colloquium the students will present intermediate and final results of their master thesis and will also participate in discussions of other thesis projects. Kind of examination A master thesis can be topically assigned without restrictions somewhere inside the faculty of engineering sciences. The processing time for a master thesis amounts to six months. The master thesis has to be drafted in German or English language and three hardcopies have to be handed in to the examination committee in time. The hardcopies have to be in DIN A4 format and they have to be bound. The master thesis shall normally consist out of 40 to 60 pages. Literature

Module Name Master-Thesis Course/Examination Name Master Thesis Colloquium Course Coordinator NN



Teaching form Presentation and discussion of the master thesis. Learning objectives The aim of the colloquium is to bring the students to be able to present the intermediate and final results of their work within a given length of time in a reasonable way. Description In the course of the accompanying colloquium, the students present the intermediate and final results of their master thesis and likewise take part in the discussions on other presented master thesis. Kind of examination Assessment of the master thesis together with the presentation of the colloquium. Literature

Imprint University of Duisburg Essen Faculty of Engineering Coordinator: Prof. Dr.-Ing. Steven X. Ding Street: Forsthausweg 2 City: 47057 Duisburg Phone: 0203 379-3386 Fax: 0203 379-2928 E-mail: [email protected] Legally binding is only the exam regulation.

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WS Winter Semester SS Summer Semester SWS Contact hours per week Cr. Credits V Lecture Ü Exercise P Laboratory S Seminar d German e English

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