Approximation Based Adaptive Tracking Control of uncertain nonholonomic mechanical Systems

In this paper, the trajectory tracking control problem of uncertain nonholonomic mechanical systems is investigated. By separately considering kinematic and dynamic models of a nonholonomic mechanical system, a new adaptive tracking control is proposed based on neural network approximation. The proposed design consists of two steps. First, the nonholonomic kinematic subsystem is transformed into a chained form, and the corresponding optimal control is derived. Second, an adaptive neural control is designed for the dynamic subsystem to make the outputs of the dynamic subsystem asymptotically track the optimal control signals chosen for the kinematic subsystem. The proposed control is simulated on a unicycle wheeled mobile robot.