inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems (Course overview)

inf340 - Uncertainty Modeling for Control in Digitalised Energy Systems (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Wintersemester 2022/2023 Examination
Lecture
  • No access 2.01.340 - Control-Oriented Modeling of Uncertainty: Stochastic and Set-Based Techniques Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Oussama Benzinane
    • Marit Lahme

    The course times are not decided yet.
    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
Exercises
  • No access 2.01.340 - Control-Oriented Modeling of Uncertainty: Stochastic and Set-Based Techniques Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Oussama Benzinane
    • Marit Lahme

    The course times are not decided yet.
    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
Project
  • No access 2.01.340 - Control-Oriented Modeling of Uncertainty: Stochastic and Set-Based Techniques Show lecturers
    • Prof. Dr.-Ing. habil. Andreas Rauh
    • Oussama Benzinane
    • Marit Lahme

    The course times are not decided yet.
    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
Hinweise zum Modul
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
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.