Design of adaptive kinematic controller using radial basis function neural network for trajectory tracking control of differential-drive mobile robot

Abstract

In this study, we propose an adaptive kinematic controller using a radial basis function neural network (RBFNN) to guide a differential-drive mobile robot (DDMR) during trajectory tracking. The kinematic controller is responsible for generating the reference values of the linear and angular velocities delivered to the robot actuators. With the fixed parameters of the kinematic controller, it is difficult for the robots to obtain acceptable performance. By using RBFNN and gradient descent method, the parameters of the kinematic controller can be updated online, thus providing smaller errors and better performance in applications. In this paper, the experimental results are presented to show the effectiveness of the proposed kinematic controller.