Longitudinal control of vehicle platoon via wavelet neural network

Transportation technology is one of the most influential areas on the human life. There has been an interest in the development of an automated highway system in which high traffic flow rates may be safely achieved. Upon entering the automated highway system, the longitudinal control of car-following collision prevention system will drive a vehicle along the fully automated highway. This paper proposes an intelligent wavelet neural network (IWNN) control system for the car-following collision prevention system based on the wavelet neural network (WNN) approach. The WNN combines the capability of artificial neural networks for learning from processes and the capability of wavelet decomposition for control dynamic systems. In the proposed IWNN system, a WNN controller is used to mimic an ideal controller and a robust controller is designed to compensate for the difference between the ideal controller and the WNN controller. The adaptation laws of the IWNN are derived in the sense of Lyapunov stability analysis, so that the stability of the control system can be guaranteed. Finally, simulation results show that the proposed IWNN control system can achieve favorable tracking performance for a safe car-following control.