inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems

inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems

Department of Computing Science 6 KP
Module components Semester courses Winter semester 2024/2025 Examination
Lecture
  • Unlimited access 2.01.340 - Uncertainty Modelling for Control in Digitalised Energy Systems Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Marit Lahme
    • Dr.-Ing. Friederike Bruns

    Thursday: 08:00 - 10:00, weekly (from 17/10/24), Location: V04 1-146
    Friday: 08:00 - 10:00, weekly (from 18/10/24), Location: V04 1-146
    Dates on Tuesday, 19.11.2024 16:00 - 18:00, Tuesday, 19.11.2024 18:00 - 20:00, Tuesday, 26.11.2024 16:00 - 18:00, Tuesday, 26.11.2024 ...(more), Location: V03 2-A208, (Industriestraße 11, PG-Raum 1-026), (I11-0-010a)

Exercises
  • Unlimited access 2.01.340 - Uncertainty Modelling for Control in Digitalised Energy Systems Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Marit Lahme
    • Dr.-Ing. Friederike Bruns

    Thursday: 08:00 - 10:00, weekly (from 17/10/24), Location: V04 1-146
    Friday: 08:00 - 10:00, weekly (from 18/10/24), Location: V04 1-146
    Dates on Tuesday, 19.11.2024 16:00 - 18:00, Tuesday, 19.11.2024 18:00 - 20:00, Tuesday, 26.11.2024 16:00 - 18:00, Tuesday, 26.11.2024 ...(more), Location: V03 2-A208, (Industriestraße 11, PG-Raum 1-026), (I11-0-010a)

Notes on the module
Prerequisites

Basic knowledge of the control of linear time-continuous and/or time-discrete systems and/or robust control

Prüfungszeiten

Following the event 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 uncertainty modelling in control systems as well as problem-specific methods for the consideration of uncertainty during simulation andobserver synthesis.
Professional competences

The students:

  • identify fundamentals of uncertainty modeling in control systems
  • characterize problem-specific solution techniques for systems with stochastic and set-based uncertainty
  • are aware of software implementations in simulation, control, and state estimation.

Methological competences
The students:

  • students identify fundamentals of uncertainty modelling in control systems
  • characterise problem-specific solution techniques for systems with stochastic and set-based uncertainty
  • are aware of software implementations in simulation, control, and state estimation.

Social competences
The students:

  • analyse problems of control-oriented uncertainty modelling
  • analyse fundamental solution techniques on a theoretical basis as well as transfer and generalise them independently toward novel research-oriented application scenarios.

Self competences
The students:

  • critically reflect the achieved results of their project work
  • acknowledge limitations of various approaches for a control-oriented uncertainty modeling.

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