Identifying Attacks on Nonlinear Cyber-Physical Systems in a Robust Model Predictive Control Setup

The design of resilient control strategies has become a crucial security issue for autonomous control systems which are exposed to the threat of attacks to both their physical and cyber components. In this paper, we present a setup consisting of two interlacing approaches towards secure control of multi-agent nonlinear dynamic systems under attack. First, we combine aspects from Pareto optimality and robust model predictive control (MPC) to maintain the system in a feasible state even if attacks with large impact occur. Second, we propose an attack identification method based on ideas from signal recovery, considering optimization problems with penalty terms for the violation of the Karush-Kuhn-Tucker conditions. We compare our approach to Nash- and Pareto-based non-robust MPC and illustrate the solution procedure with a nonlinear numerical example.