Mobile robot control using fuzzy-Gaussian neural networks

Abstract

A tracking problem for controlling the speed and azimuth of a mobile robot driven by two independent wheels is solved by using a fuzzy Gaussian neural network (FGNN) controller. The computed torque control law is first derived to obtain some relationships between the driving torque and the plant output information. To simplify the FGNN controller for the two-input two-output controlled system, a learning controller consisting of two FGNNs based on independent reasoning and a connection net with fixed weights is proposed. The effectiveness of the method is illustrated by performing the simulation of a circular trajectory tracking control.