Adaptive neural control for stochastic nonholonomic systems with full-state constraints and unknown covariance noise

Abstract

In this paper, adaptive neural control is investigated for a class of stochastic nonholonomic systems disturbed by unknown covariance noise under the condition of full-state constraints. Compared with the related literatures, this paper largely generalizes the results in recent works on stochastic nonholonomic systems. The distinctive features of this paper are three folds: the full-state constrains are introduced; the restriction assumed on incremental covariance is removed; the growth assumptions imposed on drift and diffusion terms are somewhat weakened. In the control design procedure, the barrier Lyapunov function (BLF) is applied to conquer the effect of full-state constraints to system performance, unknown covariance noise is compensated with the aid of adaptive control design, the radial basis function neural networks are utilized to approximate unknown nonlinear functions and the backstepping technique is used to construct the desired controller. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive neural controller renders the closed-loop system to be semi-globally uniformly ultimately bounded. Moreover, the system states remain in the defined compact sets and the output tracks the reference signal well. Finally, the simulation example shows the effectiveness of the proposed scheme.