Neural Control of Mobile Robot Motion Based on Feedback Error Learning and Mimetic Structure

Abstract

Mobile robots motion control is a basic problem in robotics and there are still some control difficulties such as uncertainty in a real implementation which should be considered. This paper is concerned with the neural control of wheeled mobile robots trajectory tracking and posture stabilization. In the trajectory-tracking problem, the Feedback Error Learning (FEL) structure is used and for the posture stabilization problem, the Mimetic structure is employed. These neural based structures use a classic controller, Dynamic Feedback Linearization (DFL), and help to improve the adaptiveness of it. The effectiveness of the proposed controllers is verified by simulation in Webots robotic simulator and on the e-puck which is a differential wheeled mobile robot. The simulation results verify the ability of the proposed methods for controlling the robot and handling uncertainties.