Neural Torque Controllers for Trajectory Tracking Problem of a Nonholonomic Mobile Robot

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and torque controllers is investigated. The proposed neural torque controllers (PNTCs) are based on a Gaussian radial basis function neural network (RBFNN) modeling technique, which are used to compensate the mobile robot dynamics, and bounded unknown disturbances. Also, the PNTCs are not dependent of the robot dynamics neither requires the off-line training process. The stability analysis and the convergence of tracking errors, as well as the learning algorithm for weights are guaranteed with basis on Lyapunov theory. In addition, the simulations results shows the efficiency of the PNTCs.