inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems (Complete module description)

inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems (Complete module description)

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Module label Uncertainty Modeling for Control in Digitalised Energy Systems
Modulkürzel inf340
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Technische Informatik
  • Master's programme Digitalised Energy Systems (Master) > Digitalised Energy System Design and Assessment
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Zuständige Personen
  • Rauh, Andreas (module responsibility)
  • Lehrenden, Die im Modul (Prüfungsberechtigt)
Prerequisites

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

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.
Module contents
  1. Mathematical modeling of uncertainty in linear and nonlinear dynamic systems
  2. Stochastic modeling approaches
    • Probability distributions
    • Bayesian state estimation for discrete-time systems (linear/nonlinear) and for continuous-time systems (linear)
    • Linear estimation techniques in an extended state-space (Carleman linearization for special system classes)
    • Monte-Carlo methods
  3. Estimation of states, parameters and simulation of uncertain processes
    • Outlook: Markov models
    • Outlook: Bayesian networks
  4. Set-based approaches
    • Set-based algorithms: Forward-backward contractor and bisection techniques
    • Interval methods for a verified solution of ordinary differential equations and for a stability proof of uncertain systems
    • Estimation of states and parameters as well as simulation of uncertain processes
  5. Outlook: Synthesis of controllers and state observers under an explicit description of uncertainty
Literaturempfehlungen
  • Jaulin, L., Kieffer, M., Didrit, O., Walter, E., Applied Interval Analysis, Springer- Verlag, 2001.
  • Papoulis, A.: Probability, Random Variables, and Stochastic Processes, McGraw- Hill, 4th Ed., 2002.
  • Rauh, A. Folien/ Skript zur Vorlesung „Uncertainty Modelling for Control in DES“.
Links
Language of instruction English
Duration (semesters) 1 Semester Semester
Module frequency every winter term
Module capacity unlimited
Teaching/Learning method V+Ü+P
Previous knowledge Basic knowledge of the control of linear time-continuous and/or time-discrete systems and/or robust control
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 2
Exercises 1 WiSe 1
Project 1 WiSe 1
Präsenzzeit Modul insgesamt 4 h
Examination Prüfungszeiten Type of examination
Final exam of module

Following the event period

Portfolio or written exam