inf5122 - Learning-Based Control in Digitalised Energy Systems (Course overview)

inf5122 - Learning-Based Control in Digitalised Energy Systems (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Summer semester 2025 Examination
Lecture
  • Unlimited access 2.01.5122 - Learning-Based Control in Digitalised Energy Systems Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Marit Lahme
    • Dr.-Ing. Friederike Bruns
    • Jelke Wibbeke, M. Sc.

    Tuesday: 16:00 - 18:00, weekly (from 08/04/25)
    Tuesday: 18:00 - 20:00, weekly (from 08/04/25)
    1. Iterative learning control (ILC) · Fundamental 2D system structures · Stability criteria · Selected optimization approaches 2. Data-driven neural network model-ing vs. first-principle modeling · Static function approximations · NARX modeling 3. Design of neural network-based controllers 4. Stability of neural network-based controllers
Exercises
  • Unlimited access 2.01.5122 - Learning-Based Control in Digitalised Energy Systems Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Marit Lahme
    • Dr.-Ing. Friederike Bruns
    • Jelke Wibbeke, M. Sc.

    Tuesday: 16:00 - 18:00, weekly (from 08/04/25)
    Tuesday: 18:00 - 20:00, weekly (from 08/04/25)
    1. Iterative learning control (ILC) · Fundamental 2D system structures · Stability criteria · Selected optimization approaches 2. Data-driven neural network model-ing vs. first-principle modeling · Static function approximations · NARX modeling 3. Design of neural network-based controllers 4. Stability of neural network-based controllers
Notes on the module
Prerequisites

Basic knowledge of control of linear continuous-time and/or discrete-timesystems and/or robust control
Prüfungszeiten

At the end of the course
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 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.