Duration:
2 Semester | Turnus of offer:
each winter semester | Credit points:
12 |
Course of studies, specific field and terms: - Master Robotics and Autonomous Systems 2019 (compulsory), Compulsory courses, 1st and 2nd semester
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Classes and lectures: - Model Predictive Control (exercise, 2 SWS)
- Model Predictive Control (lecture, 2 SWS)
- Real-Time Systems (exercise, 2 SWS)
- Real-Time Systems (lecture, 2 SWS)
| Workload: - 120 Hours in-classroom work
- 140 Hours private studies
- 40 Hours exam preparation
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Contents of teaching: | - Content of teaching of the course Real-Time Systems:
- Real-time processing (definitions, requirements)
- Process automation systems
- Real-time programming
- Process connectivity and networking
- Modelling of discrete event systems (automata, state charts)
- Modelling of continuous systems (differential equations, Laplace transformation)
- Application of design tools (Matlab/Simulink, Stateflow)
- Content of teaching of the course Model Predictive Control:
- LQ optimal control and Kalman filter
- Convex optimization
- Invariant sets
- Theory of Model Predictive Control (MPC)
- Algorithms for numerical optimization
- Explicit MPC
- Practical aspects (Robust MPC, Offset-free tracking, etc.)
- MPC applications
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Qualification-goals/Competencies: - Educational objectives of the course Real-Time Systems:
- The students are able to describe the fundamental problems of real-time processing.
- They are able to explain real-time computer systems for process automation, in particular SPS.
- They are able to program real-time systems in the IEC languages.
- They are able to elucidate process interfaces and real-time bus system.
- They are able to model, analyze and implement event discrete systems, in particular process control systems.
- They are able to model, analyze and implement continuous systems, in particular feedback control systems.
- They are able to make use of design tools for real-time systems.
- Educational objectives of the course Model Predictive Control:
- Students get a comprehensive introduction to methods of optimal control.
- Students get an overview of the fundamentals of numerical optimization.
- Students are able to design model predictive controllers for linear and nonlinear systems.
- Students get acquainted with several tools to implement model predictive controllers.
- Students are able to establish system theoretic properties of model predictive controllers.
- Students gain insight into possible applications areas for MPC.
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Grading through: - Written or oral exam as announced by the examiner
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Requires: |
Responsible for this module: Teachers: |
Literature: - R. C. Dorf, R. H. Bishop: Modern Control Systems - Prentice Hall 2010
- L. Litz: Grundlagen der Automatisierungstechnik - Oldenbourg 2012
- M. Seitz: Speicherprogrammierbare Steuerungen - Fachbuchverlag Leipzig 2012
- H. Wörn, U. Brinkschulte: Echtzeitsysteme - Berlin: Springer 2005
- S. Zacher, M. Reuter: Regelungstechnik für Ingenieure - Springer-Vieweg 2014
- F. Borrelli, A. Bemporad, M. Morari: Predictive Control for Linear and Hybrid Systems - Cambridge University Press, 2017 (ISBN: 978-1107016880)
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Language: - German and English skills required
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Notes:Admission requirements for taking the module:
- None
Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester.
Module Exam(s):
- RO4000-L1: Autonomous Systems, participation in the written examinations of both submodules.
- RO4001-L1: Model Predictive Control, written exam, 90 min, 50% of the module grade
- CS4160-L1: Real-Time Systems, written exam, 90min, 50% of module grade |
Letzte Änderung: 7.1.2025 |
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