Development of Autonomous Algorithm Using an Online Feedback-Error Learning Based Neural Network for Nonholonomic Mobile Robots

Abstract

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs x i , i=1～6, the hidden layer consists of two hidden units for hidden outputs o j , j=1～2, and the output layer consists of two units for the outputs τ k , k=1～2.A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, π].