Attack-Resilience Distributed Model Predictive Control of Vehicular Platoon Systems using Moving Horizon Attack Estimation

In this paper, we propose a resilient distributed model predictive control (MPC) method for a group of connected automated vehicles in the presence of cyberattacks. The malicious cyberattacks are mathematically modelled as unstructured additive terms, and the moving horizon estimation method is utilized to estimate the attack severity parameter. When cyberattacks are detected in one or more vehicles, the attacked vehicle can continue the platooning with a degraded performance by using the estimated attack severity parameter to compute the control input. The resilient control strategy handles cyberattacks using the distributed model predictive control scheme where the degraded actuator are explicitly imposed into the optimization problem as constraints. Simulation results are presented to demonstrate the effectiveness of the proposed resilient MPC method.