IJCAS

To improve the tracking efficiency of the platoon during driving and ensure the spacing safety between vehicles, a platoon tracking control strategy based on the adaptive neural network algorithm is developed. In this method, the nonlinear term in the vehicle model is estimated by the adaptive neural network, and the estimated value is used to compensate for the control input and enhance the tracking performance of the vehicle platoon. In addition, the estimation update law of target trajectory and adjacent vehicle acceleration is designed through the adaptive method, which relaxes the trajectory generation requirements of virtual vehicles, improves the tracking performance of vehicle platoon, reduces the measurement and communication burden in the platoon, and ensures the security and stability of vehicle platoon system. After constructing the vehicle and desired path model, the control objective is formulated, and the adaptive neural network algorithm controller is designed. Meanwhile, the stability of the controller is verified by the Lyapunov method. The feasibility of the proposed method is proved by simulation and experiment. Rigorous theoretical derivation and experiments confirm that the proposed strategy has obvious advantages over other existing strategies.

Keywords: Compensate, neural network, tracking control, vehicle platoon.