Dual-Observer Based Resilient Control for Vehicle Trajectory Tracking Under Tri-Modal Cyber Attacks

This study addresses vehicle trajectory tracking control under tri-modal cyber attacks, encompassing fixed sensor-to-controller/controller-to-actuator channel attacks in lateral dynamics and sparse multi-sensor attacks in position tracking. A hybrid fuzzy modeling framework is developed, integrating fuzzy logic inference with Takagi-Sugeno fuzzy techniques to approximate vehicle dynamics with time-varying velocity, payload-dependent mass, and unmeasurable cornering stiffness avoiding the conservatism inherent in conventional linear fractional transformation approaches for cornering stiffness parameterization. A dual-observer architecture combining an extended state observer and a supervisory fuzzy reduced-order observer (ESO-SFRO) is proposed for simultaneous system state reconstruction and tri-modal attack signal estimation. Based on the estimated states, a cyber-resilient controller is designed to ensure lateral stability and trajectory tracking accuracy. Experimental validation via CarSim/Simulink co-simulation demonstrates the proposed ESO-SFRO based controller exhibits superior dynamic stability and trajectory tracking performance under coupled cyber-physical disturbances.