Scalable Robust Safety Filter With Unknown Disturbance Set

• 成果类型: Article
• 署名作者: Gruber, Felix; Althoff, Matthias
• 署名单位: Technical University of Munich
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3292329
• 发表日期: 2023
• 页码: 7756-7770
• 关键词: Optimal control; reachability analysis; Robust control; Supervisory control; System identification

摘要

Equipping any controller with formal safety guarantees can be achieved by using safety filters. These filters modify the desired control input in the least restrictive way to guarantee safety. However, it is an unresolved issue to construct scalable safety filters without assuming the availability of the disturbance set. In this article, we address this issue by proposing an efficient approach to implementing safety filters. In particular, we perform offline set membership identification to obtain a linear model that is conformant to a finite set of training data. Based on this conformant model, we compute a set-based safe backup controller with a corresponding safe set. Because a new measurement obtained online might invalidate the model conformance, we update the model, the safe backup controller, and the safe set online to restore formal safety guarantees. We use scalable reachability analysis and convex optimization algorithms to perform these updates as quickly as possible. We demonstrate the usefulness and scalability of our safety filter approach using four numerical examples from the literature.