Neural predictive control for a car-like mobile robot

Abstract

This paper presents a new path tracking scheme for a car-like mobile robot based on neural predictive control. A multi-layer back-propagation neural network is employed to model non-linear kinematics of the robot instead of a linear regression estimator in order to adapt the robot to a large operating range. The neural predictive control for path tracking is a model-based predictive control based on neural network modelling, which can generate its output in terms of the robot kinematics and a desired path. The desired path for the robot is produced by a polar polynomial with a simple closed form. The multi-layer back-propagation neural network is constructed by a wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problem caused by the local minima when training the neural network. The wavelet neural network has the advantage of using an explicit way to determine the number of the hidden nodes and initial value of weights. Simulation results for the modelling and control are provided to justify the proposed scheme.