inf5106 - Optimal and Model-Predictive Control (Course overview)

inf5106 - Optimal and Model-Predictive Control (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Summer semester 2025 Examination
Lecture
  • Unlimited access 2.01.5106 - Optimal and Model-Predictive Control Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Dr.-Ing. Friederike Bruns
    • Marit Lahme
    • Jelke Wibbeke, M. Sc.

    Tuesday: 12:00 - 14:00, weekly (from 08/04/25)
    Thursday: 08:00 - 10:00, weekly (from 10/04/25)
    1. Parameter optimization · Unconstrained optimisation · Optimisation under equality/ inequality constraints 2. Dynamic optimisation (structural optimi-sation) · Bellman’s optimality principle · Maximum principle of Pontryagin · Special optimisation problems: Mini-mum time problems, minimum energy, LQR 3. Linear model-predictive control 4. Nonlinear model-predictive control 5. Receding horizon state estimation
Exercises
  • Unlimited access 2.01.5106 - Optimal and Model-Predictive Control Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Dr.-Ing. Friederike Bruns
    • Marit Lahme
    • Jelke Wibbeke, M. Sc.

    Tuesday: 12:00 - 14:00, weekly (from 08/04/25)
    Thursday: 08:00 - 10:00, weekly (from 10/04/25)
    1. Parameter optimization · Unconstrained optimisation · Optimisation under equality/ inequality constraints 2. Dynamic optimisation (structural optimi-sation) · Bellman’s optimality principle · Maximum principle of Pontryagin · Special optimisation problems: Mini-mum time problems, minimum energy, LQR 3. Linear model-predictive control 4. Nonlinear model-predictive control 5. Receding horizon state estimation
Notes on the module
Prüfungszeiten

at the end of the lecture period
Module examination

Portfolio or written exam; contents of portfolio will be announced at the beginning of the lecture period
Skills to be acquired in this module

The students identify fundamentals of the optimisation of control systems
Professional competence
The students:

  • identify fundamentals of the optimisation of control systems
  • characterise static and dynamic optimisation problems
  • are aware of software implementations for selected test rigs


Methological competence
The students:

  • analyse problems of optimal control
  • generalise them independently toward novel research-oriented application scenarios


Social competence
The students:

  • develop solution ideas for real control engineering tasks in small groups in a project/practical course accompanying the lecture
  • communicate their results in short presentations


Self competence
The students:

  • critically reflect the achieved results of their project work
  • acknowledge limitations of various approaches for optimal control design