Wavelet neural network based predictive control for mobile robots

Abstract

This paper presents a predictive control scheme for mobile robots that possess complexity, non-linearity and uncertainty. A multi-layer back-propagation neural network is employed as a model for nonlinear dynamics of the robot. The control variables are produced by optimizing the performance index on-line using the steepest gradient descent algorithm. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization. The wavelet network is also helpful to determine the number of the hidden nodes and the initial value of weights. The sparse train data in our path tracking case can reduce the effect of the "curse of dimensionality" on the network size in high dimensional function learning caused by the orthogonal wavelet base function.