Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives.

Abstract

In the era of Industrial 4.0, the next-generation control system regards the cyber-physical system (CPS) as the core ingredient thanks to the comprehensive integration of physical systems, online computation, networking and control. A reliable, stable and resilient CPS should pledge robustness and safety. A significant concern in CPS development arises from security issues since the CPS is vulnerable to physical constraints, ubiquitous uncertainties and malicious cyber attacks. The integration of the stochastic model predictive control (MPC) framework and the resilient mechanism is a possible approach to guarantee robustness in the presence of stochastic uncertainties and enable resilience against cyber attacks. This review paper aims to offer a detailed overview of existing stochastic MPC algorithms and their CPS applications. More specifically, we first review existing stochastic MPC algorithms for both linear and nonlinear systems subject to probabilistic constraints. We then discuss how to extend the stochastic MPC framework to incorporate resilience mechanisms for constrained CPS under various malicious attacks. Finally, we present an architectural stochastic MPC-based framework for resilient CPS and identify future research challenges. This article is part of the theme issue 'Towards symbiotic autonomous systems'.