Trajectory tracking control of a mobile robot by computed torque method with on-line learning neural network

Abstract

This paper proposes a novel control algorithm for the mobile robot with nonholonomic constraint. The algorithm consists of two control loops: one is based on the kinematics and Lyapunov theory to derive the control laws for the tangent and angular velocities to control the robot to follow a target trajectory, the other controls the robot dynamic based on the moment method in which a neural network namely RBFNN is introduced to compensate the uncertainty of dynamic parameters. The convergence of the estimators based on RBFNN of Stone-Weierstrass is proven. The asymptotically stabilization of the whole system is confirmed by direct Lyapunov stabilization theory. The effectiveness of the method is verified by simulations in Matlab.