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arXiv · 2211.12628

Safe Control and Learning Using Generalized Action Governor

Peiyuan Fang·Weiqi Zhang·Lu Xiong·Nan Li·Yanjun Huang·Yutong Li·Ilya Kolmanovsky·Anouck Girard·H. Eric Tseng·Dimitar Filev
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Abstract

This paper introduces the Generalized Action Governor (AG), a supervisory scheme that augments a nominal closed-loop system with the capability to enforce state and input constraints through online action adjustment. We develop a generalized AG theory for discrete-time systems under bounded uncertainties, and relax the usual requirement of positive invariance to returnability of a safe set. Based on the theory, we present tailored AG design procedures for linear systems and for discrete systems with finite state and action spaces. We further study safe online learning enabled by the AG and present two safe learning strategies, namely safe Q-learning and safe data-driven Koopman operator-based control, both integrated with the AG to guarantee constraint satisfaction during learning. Numerical results illustrate the proposed methods.

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Peiyuan Fang, Weiqi Zhang, Lu Xiong, Nan Li, Yanjun Huang, Yutong Li, Ilya Kolmanovsky, Anouck Girard, H. Eric Tseng, Dimitar Filev. 2022-11-22. Safe Control and Learning Using Generalized Action Governor. https://arxiv.org/abs/2211.12628

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