![Thumbnail](/bitstream/handle/20.500.11850/513889/1-s2.0-S2405896321005206-main.pdf.jpg?sequence=5&isAllowed=y)
Open access
Date
2021Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000454950Publication status
publishedExternal links
Editor
Book title
24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020)Journal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Networked Control Systems; Linear Systems; Safe learningOrganisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Funding
SEED-19 18-2 - Collaborative Exploration-Exploitation: Distributed Decision-making and Estimation in Robotic Networks (ETHZ)
Notes
Conference cancelled due to Corona virus (COVID-19).More
Show all metadata
ETH Bibliography
yes
Altmetrics