Output feedback trajectory tracking control of a car-like drive wheeled mobile robot using RBF neural network

Abstract

This paper addresses the output feedback trajectory tracking control problem of Ackerman steering-drive wheeled mobile robots under nonholonomic constraints in the presence of model uncertainties without velocity measurement. A RBF neural network and a linear observer are employed to construct the controller for constrained robot with only position measurement. The proposed controllers employ saturation-type adaptive-neural control laws to effectively compensate for the uncertain parameters, unmodeled dynamics and unknown bounded disturbances. Lyapunov-based stability analyses are utilized to guarantee that tracking errors are uniformly ultimately bounded and exponentially converge to a small ball containing the origin. The simulation results are presented to illustrate the tracking effectiveness of the controller.