Resilient distributed model predictive control of state‐delayed linear parameter varying systems with quantitative communication against denial of service attacks

Abstract

This work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distributed MPC (DMPC) to achieve the global performance of centralized MPC (CMPC). However, control performance can be severely degraded by unreliable communication networks, for example, with denial of service (DoS) attacks. A resilient control framework is derived to address the unreliable communications in DMPC. A global system is divided into subsystems for the distributed control purpose. To deal with the model uncertainties and state delays, a “min‐max” DMPC algorithm is presented with a buffer to ensure resilience against DoS attacks. A quantization scheme is introduced to quantize the control information exchanged between subsystems. An iterative interaction scheme is proposed to exchange feedback control laws among subsystems. The stability of the closed‐loop system under the proposed algorithm is ensured by using a Lyapunov function method. The effectiveness of the proposed DMPC is demonstrated through two simulation examples.