学术文献库

This article introduces a general framework for safe control and learning based on the generalized action governor (AG). The AG is a supervisory scheme for augmenting a nominal closed-loop system with the ability of strictly handling prescribed safety constraints. In the first part of this article, we present a generalized AG methodology and analyze its key properties in a general setting. Then, we introduce tailored AG design approaches derived from the generalized methodology for linear and discrete systems. Afterward, we discuss the application of the generalized AG to facilitate safe online learning, which aims at safely evolving control parameters using real-time data to enhance control performance in uncertain systems. We present two safe learning algorithms based on, respectively, reinforcement learning and data-driven Koopman operator-based control integrated with the generalized AG to exemplify this application. Finally, we illustrate the developments with a numerical example.