inf300 - Hybrid Systems

inf300 - Hybrid Systems

Department of Computing Science 6 KP
Module components Semester courses Summer semester 2024 Examination
Lecture
  • Unlimited access 2.01.300 - Hybride Systeme Show lecturers
    • Janis Kröger, M. Sc.
    • Prof. Dr. Martin Georg Fränzle
    • Paul Kröger

    Tuesday: 14:00 - 16:00, weekly (from 02/04/24)
    Friday: 08:00 - 10:00, weekly (from 05/04/24)

Exercises
  • Unlimited access 2.01.300 - Hybride Systeme Show lecturers
    • Janis Kröger, M. Sc.
    • Prof. Dr. Martin Georg Fränzle
    • Paul Kröger

    Tuesday: 14:00 - 16:00, weekly (from 02/04/24)
    Friday: 08:00 - 10:00, weekly (from 05/04/24)

Hinweise zum Modul
Prüfungszeiten

At the end of the lecture period

Module examination

Semester project including written work and final presentation

Skills to be acquired in this module

The module gives an introduction to hybrid discrete-continuous systems, as arising by embedding digital hardware into physical environments, and it elaborates on state of the art methods for the mathematical modelling and the analysis of such systems. It thus provides central competences for understanding and designing reliable cyber-physical systems.
Professional competence
The students:

  • characterise formal models of cyber-physical systems: hybrid automata, hybrid state transition systems
  • name domain-specific system requirements: safety, stability, robustness
  • name analysis methods: symbolic state-space exploration, abstraction and abstraction refinement, generalized Lyapunov-Methods
  • use state-of-the-art analysis tools
  • select and apply adequate modelling and analysis methods for concrete application scenarios
  • apply methods to reduce large state spaces and reduce infinite-state systems by abstraction
  • know the de-facto industry standards for system modelling and are able to apply the corresponding modelling framewortks and tools

Methodological competence
The students:

  • mtdel heterogeneous dynamical systems with adequate modelling and design tools, in particular Simulink/Stateflow
  • transfer modelling and analysis methods to other heterogeneous domains, e.g. socio-technical systems

Social competence
The students:

  • work in teams
  • solve complex modelling, design, and analysis tasks in teams

Self-competence
The students:

  • reflect their actions and respect the scope of methods dedicated to hybrid systems

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