Robust Control Methods for Vehicle Trajectory Tracking Amid Multi-Channel Network Attacks

Network attacks present a critical threat to vehicle trajectory tracking systems, especially when multi-channel attacks can severely compromise system stability and tracking accuracy. Addressing these issues is challenging due to the complexity of vehicle dynamics, attack signal suppression, and reliable tracking on roads with varying curvature. This paper proposes an advanced control strategy based on a fuzzy extended observer to mitigate multi-channel network attacks. A hybrid model integrating fuzzy inference with Takagi-Sugeno (T-S) techniques is developed to capture trajectory tracking dynamics. A fuzzy extended observer estimates attack impacts on vehicle dynamics, and a fuzzy controller is designed to counteract these attacks while ensuring stable tracking. The stability analysis confirms the stability of the system. Finally, co-simulations in CarSim and Simulink demonstrate the practical effectiveness of the proposed control strategy.