Stud.IP Uni Oldenburg
University of Oldenburg
27.10.2021 09:40:10
inf300 - Hybrid Systems (Course overview)
Department of Computing Science 6 KP
Module components Semester courses Sommersemester 2020 Examination
Lecture
  • No access 2.01.300 - Hybride Systeme Show lecturers
    • Rabeaeh Kiaghadi
    • Prof. Dr. Martin Georg Fränzle
    • Paul Kröger

    Tuesday: 12:00 - 14:00, weekly (from 21/04/20), V
    Friday: 08:00 - 10:00, weekly (from 24/04/20), Ü

Exercises
  • No access 2.01.300 - Hybride Systeme Show lecturers
    • Rabeaeh Kiaghadi
    • Prof. Dr. Martin Georg Fränzle
    • Paul Kröger

    Tuesday: 12:00 - 14:00, weekly (from 21/04/20), V
    Friday: 08:00 - 10:00, weekly (from 24/04/20), Ü

Notes for the module
Prerequisites
Kenntnisse aus dem BSc.-Studiengang Informatik mit Vertiefungsrichtung "Eingebettete Systeme und Mikrorobotik" bzw. entsprechende Kenntnisse aus den Angleichungsmodulen des MSc.-Studiengangs.Begründung: Die Vorlesung setzt Kenntnisse der Modellierung and Analyse reaktiver Systeme voraus.
Time of examination
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