Plug-and-Play Distributed Safety Verification for Linear Control Systems With Bounded Uncertainties

Abstract

Control and, in particular, learning-based control, is challenging in large-scale and safety-critical system networks due to interactions between subsystems, which can potentially be time varying. This article presents a plug-and-play safety framework that can be applied together with high-performance control algorithms, e.g., emerging from learning techniques. The framework ensures constraint satisfaction for a network of uncertain linear control systems during control, learning, and during topology changes in the form of agents joining or leaving the network with prior plug-in or plug-out requests. The presented approach is based on safe sets and tube-based tracking controllers, which can be designed in a distributed fashion, i.e., using only local information. The presented plug-and-play procedure requires only the update of the safe sets of the agents directly involved in the plug-and-play operation making the operation computationally efficient compared to completely redesigning all safe sets. The capabilities of the safety framework are illustrated using numerical examples.