Open access
Datum
2021Typ
- Conference Paper
ETH Bibliographie
yes
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Abstract
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000454950Publikationsstatus
publishedExterne Links
Herausgeber(in)
Buchtitel
24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020)Zeitschrift / Serie
IFAC-PapersOnLineBand
Seiten / Artikelnummer
Verlag
ElsevierKonferenz
Thema
Networked Control Systems; Linear Systems; Safe learningOrganisationseinheit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Förderung
SEED-19 18-2 - Collaborative Exploration-Exploitation: Distributed Decision-making and Estimation in Robotic Networks (ETHZ)
Anmerkungen
Conference cancelled due to Corona virus (COVID-19).ETH Bibliographie
yes
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