Trajectory tracking of a nonholonomic mobile robot: A suggested neural torque controller based on the sliding mode theory

Abstract

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and a neural torque controller is proposed. The suggested neural torque controller (SNTC), based on the sliding mode theory, is constituted by a dynamic neural controller (DNC) and a robust neural compensator (RNC). The SNTC is designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs), and applied to compensate the mobile robot dynamics, bounded unknown disturbances, neural network modeling errors, and influence of payload. To alleviate the problems met in practical implementation using classical sliding mode controllers and to eliminate the chattering phenomenon is used the RNC of the SNTC, which is nonlinear and continuous, in lieu of the discontinuous part of the control signals present in classical forms. Also, the SNTC neither requires the knowledge of the mobile robot dynamics nor the time-consuming training process. Stability and convergence analysis of tracking errors, as well as the learning algorithms (for weights, centers, and widths) are guaranteed based on the Lyapunov method. Numerical simulations are provided to show the effectiveness of the suggested approach.