Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.