A data-driven method for computing polyhedral invariant sets of black-box switched linear systems
Abstrak
In this paper, we consider the problem of invariant set computation for black-box switched linear systems using merely a finite set of observations of system trajectories. In particular, this paper focuses on polyhedral invariant sets. We propose a data-driven method based on the one step forward reachable set. For formal verification of the proposed method, we introduce the concepts of $λ$-contractive sets and almost-invariant sets for switched linear systems. The convexity-preserving property of switched linear systems allows us to conduct contraction analysis on the computed set and derive a probabilistic contraction property. In the spirit of non-convex scenario optimization, we also establish a chance-constrained guarantee on set invariance. The performance of our method is then illustrated by numerical examples.
Topik & Kata Kunci
Penulis (2)
Zheming Wang
Raphaël M. Jungers
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓