WNN-based intelligent transportation control system

Abstract

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and 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 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. Moreover, based on robust control technique, the compensation controller is developed to attenuate the effect of the approximation error, so that the desired attenuation level can be achieved. Finally, it is applied to control a marine transportation system. The simulation results demonstrate that the proposed control system can achieve favorable control performance than other control methods. © 2010 Springer-Verlag Berlin Heidelberg.