Compositional Construction of Barrier Functions for Switched Impulsive Systems

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Katharina Bieker - , Ludwig Maximilian University of Munich (Author)
  • Hugo Kussaba - , Technical University of Munich (Author)
  • Philipp Scholl - , Ludwig Maximilian University of Munich (Author)
  • Jaesug Jung - , Technical University of Munich (Author)
  • Abdalla Swikir - , Omar Al-Mukhtar University, Technical University of Munich (Author)
  • Sami Haddadin - , Aalto University, Mohamed Bin Zayed University of Artificial Intelligence, TUD Dresden University of Technology (Author)
  • Gitta Kutyniok - , Ludwig Maximilian University of Munich, Munich Center for Machine Learning (MCML), University of Tromsø – The Arctic University of Norway, German Aerospace Center (DLR) (Author)

Abstract

Many systems occurring in real-world applications, such as controlling the motions of robots or modeling the spread of diseases, are switched impulsive systems. To ensure that the system state stays in a safe region (e.g., to avoid collisions with obstacles), barrier functions are widely utilized. As the system dimension increases, deriving suitable barrier functions becomes extremely complex. Fortunately, many systems consist of multiple subsystems, such as different areas where the disease occurs. In this work, we present sufficient conditions for interconnected switched impulsive systems to maintain safety by constructing local barrier functions for the individual subsystems instead of a global one, allowing for much easier and more efficient derivation. To validate our results, we numerically demonstrate its effectiveness using an epidemiological model.

Details

Original languageEnglish
Title of host publication2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages7085-7091
Number of pages7
ISBN (electronic)979-8-3503-1633-9, 979-8-3503-1632-2
ISBN (print)979-8-3503-1634-6
Publication statusPublished - Feb 2025
Peer-reviewedYes

Publication series

SeriesProceedings of the IEEE Conference on Decision and Control
ISSN0743-1546

Conference

Title63rd IEEE Conference on Decision and Control
Abbreviated titleCDC 2024
Conference number63
Duration16 - 19 December 2024
Website
LocationMilan Convention Centre
CityMilan
CountryItaly