inf5122 - Learning-Based Control in Digitalised Energy Systems (Complete module description)

Module label	Learning-Based Control in Digitalised Energy Systems
Module code	inf5122
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Computing Science (Master) > Angewandte Informatik Master's programme Digitalised Energy Systems (Master) > Digitalised Energy System Design and Assessment Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons	Rauh, Andreas (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	Basic knowledge of control of linear continuous-time and/or discrete-timesystems and/or robust control
Skills to be acquired in this module	The students identify fundamentals of learning-based control for dynamic systems. Professional competences The students: identify fundamentals of learning-based control for dynamic systems characterise problem- specific learning techniques are aware of software implementations for selected test rigs. Methological competences The students: analyse problems of learning-based control generalise them independently toward novel research-oriented application scenarios. Social competences The students: develop solution ideas for real-life control problems within an accompanying project/lab course in small teams explain the obtained results in short presentations. Self competences The students: critically reflect the achieved results of their project work acknowledge limitations of various approaches for learning-based control design.
Module contents	Iterative learning control (ILC) Grundlegende 2D-Systemstrukturen Stability criteria Ausgewählte Optimierungsansätze Data-driven neural network modelling vs. first-principle modelling Static function approximations NARX-modelling Design of neural network- based controllers Stability of neural network-based controllers
Recommended reading	Moore, K.L. Iterative Learning Control for Deterministic Systems. London: Springer- Verlag. 1993 Jian Xin Xu; Ying Tan. Linear and Nonlinear Iterative Learning Control. Springer- Verlag. 2003 Bristow, D. A.; Tharayil, M.; Alleyne, A. G. "A Survey of Iterative Learning Control A learning-based method for high-performance tracking control". IEEE control systems magazine. Vol. 26. pp. 96–114. 2006 The Mathworks Inc. Deep Learning Toolbox – Documentation, 2021 Rauh, A. Folien/ Skript zur Vorlesung „Learning-Based Control in DES“
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	every summer term
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	SuSe	28
Exercises	2	SuSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the course	Portfolio or written exam; contents of portfolio will be announced at the beginning of the lecture period