Adaptive Radial Basis Function-Based Sliding Mode Control for Platoons Under Multiple Attacks

This paper investigates the homogeneous platoon control problem under multiple cyber attacks. Cyber attacks can lead to collisions or degradations to the vehicle platoons. To deal with this problem, an adaptive Radial Basis Function (RBF) sliding mode control method is designed for the attacked vehicle platoons. Then design the adaptive sliding mode controllers to achieve the control objective: the spacing error converges to zero, and the platoon maintains the string stability. Using RBF neural networks to approximate the unknown acceleration information when the platoons are attacked by False Data Injection (FDI) attacks. The adaptive control law is derived according to Lyapunov stability law. Sufficient conditions of string stability are obtained, and string stability is proved. Numerical simulation results showed the effectiveness of the proposed method.