Distributed Data-Driven Secure Cooperative Control for a Connected Homogeneous Vehicle Platoon Under Denial-of-Service Attacks

This paper investigates a distributed data-driven secure coordination (DDDSC) control strategy for a connected homogeneous vehicle (CHV) platoon under denial of service (DoS) attacks. First, a DoS attack model is established based on the characteristics of DoS attacks, and a corresponding attack compensation algorithm is given. Then, a novel distributed data-driven CHV platoon coordination control method is designed using a radial basis function neural network (RBFNN). The convergence speed and accuracy of the control algorithm are enhanced by approximating and predicting the time-varying parameter pseudo gradient (PG) that characterizes the input-output relationship estimation. Theoretical analysis shows that the proposed algorithm ensures individual vehicles' internal stability and the platoon system's string stability when subjected to DoS attacks. Finally, a group of virtual simulation control experiments compares the tracking control effects of the CHV platoon under different algorithms in the presence of the same DoS attack, verifying the effectiveness and practicality of the proposed DDDSC control method.