Model Predictive Control and Machine Learning

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 Dr.-Ing. Anton Savchenko , Prof. Dr.-Ing. Rolf Findeisen
Assistants Maik Pfefferkorn , Hoang Hai Nguyen
Semester WiSe (2+1)
Teaching language English
Prerequisit Basic concepts of control theory. Fundamentals of linear algebra, differential equations, fundamentals of optimal control.
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
  • J. Rawlings, D. Mayne, and M. Diehl. Model predictive control: theory, computation, and design. Nob Hill Publishing, ISBN: 978-0975937709, 2020.
  • S. Raković, and W. Levine. Handbook of Model Predictive Control. Birkhäuser Basel, ISBN: 978-3-319-77488-6, 2018.
  • C.E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006. ISBN 0-262-18253-X.

Content of the Lecture

Aim of this course

The students will understand the basics concepts of model predictive control (MPC). Furthermore, they are familiarized with machine learning approaches that can support model predictive controllers and possibly enhance the controller performance. This entails knowledge about theoretical questions such as stability in the nominal case, as well as extensions to the case of uncertain and disturbed systems. The students are enabled to design and implement model predictive controllers based on first principle/physical or data-based/machine learning based models. This entails the setup and design of the control structure as well as the tuning and identification of suitable parameters and cost functions of the controller.


  • Introduction and basics of optimal control
  • Linear Quadratic Regulator (LQR) discrete-time/continuous-time
  • Basics of model predictive control (MPS) (cost functions, constraints, receding horizon)
  • Nominal model predictive control of linear systems
  • Robust and stochastic model predictive control of linear systems
  • Control of nonlinear systems with model predictive control
  • Basics of machine learning
  • Combination of machine learning approaches with model predictive control


All materials of the lecture and exercises are provided via Moodle.
Exam winter term 2022/2023
Form of exam written or oral
Date expected March 2023
Time tba
Room tba
Permitted resources pen
Access to written exam tba