Probabilistic fault‐tolerant fuzzy control for adaptive event‐triggered lane‐keeping system of autonomous electric vehicles

Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles, reducing system reliability and posing significant challenges to driving safety. To enhance the system resilience, this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger (AET) mechanism to realize stable, reliable, and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults. First, to capture the uncertain and time-varying nature of tire cornering stiffness, an effective Takagi-Sugeno (T-S) fuzzy tire model is developed. Then, by employing the distribution-based probabilistic approach, two sets of unrelated random variables, random sensor and actuator faults in the control system, are modeled. Next, to improve communication efficiency and address ineluctable network-induced delays, an AET control framework with a well-designed triggering condition is established. Subsequently, a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H ∞ performance is designed by using the Lyapunov-Krasovski functional method. Furthermore, the mean-square exponential stability of the closed-loop system is rigorously guaranteed. Finally, real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and effectiveness of the proposed control strategy.