Machine Learning for Mechatronic and Dynamical Systems

Up-to-date information will be distributed via Moodle only!

Please register for the lecture via the TUCaN system of TU Darmstadt. If you have problems accessing Moodle, please contact

General Information about the Course

Lecturer Prof. Dr.-Ing. Rolf Findeisen , Dr.-Ing. Anton Savchenko ,
Assistants Hendrik Alsmeier , Sebastian Hirt
Semester SoSe (2+1), Start of lecture: Wed, 25 April 2024
Teaching language English
Prerequisit Basic concepts of control theory. Fundamentals of linear algebra, differential, and difference equations. Knowl-
edge in Python and/or Matlab.
Form of examination written or oral
The examination takes place in writing or orally, depending on the number of examination registrations. The form of the examination will be announced shortly after the end of the registration period.
Old exams
Subsequent lectures
Recommended literature
  • Brunton, Steven L., and J. Nathan Kutz. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2019.
  • D. Bertsekas. Reinforcement Learning and Optimal Control. Athena Scientific, 2019.
  • K. P. Murphy. Probabilistic Machine Learning: An Introduction. MIT Press, 2022

Content of the Lecture

Aim of this course

The lecture introduces the fundamental concepts of machine learning, focusing on applications in mechatronics and dynamical systems, including data-driven and hybrid modeling, simulation, monitoring, planning, decision making, optimization, and control.

Topics

  • Machine learning in mechatronics and dynamical systems
  • Basics of machine learning
  • Review of dynamical systems with a machine learning perspective
  • Machine learning from an optimization perspective
  • Regression
  • Clustering (regression and non-regression based)
  • Support vector machines
  • Gaussian processes
  • Neural Networks
  • Optimal control and reinforcement learning
  • Safety and reliability of machine learning for dynamical systems
  • Application examples from machine learning in control

Organization

All materials of the lecture and exercises are provided via Moodle.
Exam summer term 2024
Form of exam written or oral
Date expected September 2024
Time tba
Room tba
Permitted resources pen
Access to written exam tba