Output Feedback Trajectory Tracking Control of a Car-like Drive Wheeled Mobile Robot Using RBF Neural Network

This paper addresses the output feedback trajectory tracking control problem of Ackerman steering-drive wheeled mobile robots under non-holonomic constraints in the presence of model uncertainties without velocity measurement. By determining a suitable set of output equations, an input–output model of wheeled mobile robots is developed, which helps the designer utilize the classic control algorithms of robot manipulators. 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.