Adaptive Neural Networks Control of Intelligent Vehicle Under Physical Fault and Stealthy Replay Attack Threats

With the continuous advancement of intelligent connected vehicle technology, the presence of vehicle faults and network attacks poses significant threats to the reliability and security of electric vehicle. This paper proposes an adaptive neural network (NN) fault-tolerant controller design for vehicle, and employs the dynamic watermarking method to detect stealthy replay attacks. First, a vehicle dynamics model is established considering actuator faults and unknown disturbances. Then, a fault-tolerant controller is designed by integrating an adaptive NN and a nonlinear disturbance observer (DO) to handle faults and external disturbances. Next, by incorporating dynamic watermarking into the controller, a residual-based detection function is designed to detect stealthy replay attacks. Subsequently, the convergence of the proposed control algorithm is proven using the direct Lyapunov method. Finally, hardware-in-the-loop (HIL) tests validate the effectiveness of the proposed control method.