Gaussian Process Based Trajectory Tracking for Nonholonomic Robots with Velocity Constraints

Abstract

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for nonholononmic robots subject to velocity input uncertainties and velocity input constraints. Gaussian process regression (GPR) is employed on account of its powerful estimation ability and wide scope of applications as a nonparametric regression method. The velocity uncertainties are estimated online using the real-time measured data. The prediction mean and variance of the GPR are used to counteract the effect of the uncertainties and to design the robust control term, respectively. A continuous robust controller is proposed which has the advantage of achieving asymptotic convergence of the trajectory tracking error instead of uniformly ultimately boundedness. Moreover, the velocity constraints can be handled by the design parameters. Simulation examples illustrate the effectiveness of the proposed controller.