An efficient neural controller for a nonholonomic mobile robot

In this paper, a novel neural network based controller is developed for real-time fine motion control of a nonholonomic mobile robot with completely unknown robot dynamics and under unmodeled disturbance. By taking advantage of the robot regressor dynamics that express the highly nonlinear robot dynamics in a linear form in terms of the robot dynamic parameters, the neural network consists of a single layer feedforward structure, and the learning algorithm is computationally efficient. Unlike previous works that use a typical backstepping velocity planner as the control input, a novel neural dynamics based velocity planner is used as input. The stability of the proposed control system and the convergence of tracking errors to zero are rigorously proved using the Lyapunov theory. The fine control of mobile robot is achieved through the online learning of the neural network without any off-line learning procedures. The effectiveness and efficiency of the proposed controller is demonstrated by simulation studies.