Neural Control Applied to the Problem of Trajectory Tracking of Mobile Robots with Uncertainties

Abstract

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains parametric and nonparametrics uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and were designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is applied to compensate the uncertainties of the mobile robot. The TNC, based on the computed torque control, is applied to compensate the mobile robot dynamics, significant uncertainties, bounded unknown disturbances, neural networks modeling errors, influence of payload, and unknown kinematic parameters. Also, the PNC are not dependent of the mobile robot kinematics and dynamics neither require the off-line training process. Stability analysis and convergence of tracking errors to zero, as well as the learning algorithms for weights, centers, and variances (becoming nonlinearly parameterized RBFNNs) are guaranteed with basis on Lyapunov theory. In addition, the simulations results are provided to show the efficiency of the PNC.