Direct adaptive neural tracking control for a class of stochastic pure-feedback nonlinear systems with unknown dead-zone

This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure-feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed-loop system are semi-globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright (C) 2012 John Wiley & Sons, Ltd.