Module RO4000-KP12
Autonomous Systems (AS)
Duration
2 Semester
Turnus of offer
each winter semester
Credit points
12
Course of studies, specific fields and terms:
- Master Robotics and Autonomous Systems 2019, compulsory, Compulsory courses
Classes and lectures:
- RO4001-Ü: Model Predictive Control (exercise, 2 SWS)
- CS4160-Ü: Real-Time Systems (exercise, 2 SWS)
- RO4001-V: Model Predictive Control (lecture, 2 SWS)
- CS4160-V: Real-Time Systems (lecture, 2 SWS)
Workload:
- 120 hours in-classroom work
- 140 hours private studies
- 40 hours exam preparation
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
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.
Grading through:
- Written or oral exam as announced by the examiner
Requires:
Responsible for this module:
- Prof. Dr. Georg Schildbach
- Prof. Dr.-Ing. Mladen Berekovic
Teacher:
- Institute for Electrical Engineering in Medicine
- Institute of Computer Engineering
- Prof. Dr.-Ing. Mladen Berekovic
- MitarbeiterInnen des Instituts
- Prof. Dr. Georg Schildbach
- MitarbeiterInnen des Instituts
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)
Language:
- German and English skills required
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
Last Updated:
23.02.2026