Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410183 | Neurocomputing | 2011 | 11 Pages |
Abstract
This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhaoxu Yu, Hongbin Du,