This is not the latest version of this item. The latest version can be found at: https://www.research-collection.ethz.ch/handle/20.500.11850/513889
- Conference Paper
In large-scale networks of uncertain dynamical systems, where communication is limited and there is a strong interaction among subsystems, learning local models and control policies offers great potential for designing high-performance controllers. At the same time, the lack of safety guarantees, here considered in the form of constraint satisfaction, prevents the use of data-driven techniques to safety-critical distributed systems. This paper presents a safety framework that guarantees constraint satisfaction for uncertain distributed systems while learning. The framework considers linear systems with coupling in the dynamics and subject to bounded parametric uncertainty, and makes use of robust invariance to guarantee safety. In particular, a robust non-convex invariant set, given by the union of multiple ellipsoidal invariant sets, and a nonlinear backup control law, given by the combination of multiple stabilizing linear feedbacks, are computed offline. In presence of unsafe inputs, the safety framework applies the backup control law, preventing the system to violate the constraints. As the robust invariant set and the backup stabilizing controller are computed offline, the online operations reduce to simple function evaluations, which enables the use of the proposed framework on systems with limited computational resources. The capabilities of the safety framework are illustrated by three numerical examples. Show more
Journal / seriesIFAC-PapersOnLine
Pages / Article No.
SubjectNetworked Control Systems; Linear Systems; safe learning
Organisational unit09563 - Zeilinger, Melanie / Zeilinger, Melanie
NotesCancellation of the postponed 24th MTNS which was due to be held in Cambridge in August 2021 due to the Corona virus (COVID-19).
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