A data‐driven safety preserving control architecture for constrained cyber‐physical systems

Abstract In this article, we propose a data‐driven networked control architecture for unknown and constrained cyber‐physical systems capable of detecting networked false‐data‐injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data‐driven outer approximation of the expected robust one‐step reachable set. On the other hand, on the plant's side, we design a data‐driven safety verification module, which resorts to worst‐case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data‐driven set‐theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre‐established safe configuration until an attack‐free condition is recovered. Numerical simulations involving a two‐tank water system illustrate the features and capabilities of the proposed control architecture.