| 摘要: |
|
| 关键词: |
| DOI： |
| Received:September 19, 2008Revised:June 17, 2010 |
| 基金项目:This work was supported by the Beijing Education Committee Cooperation Building Foundation Project (No. XK100070532). |
|
| Adaptive backstepping control for a class of semistrict feedback nonlinear systems using neural networks |
| Hongwei YANG,Zhiping LI |
| (School of Automation, Beijing Institute of Technology) |
| Abstract: |
| This paper addresses a neural adaptive backstepping control with dynamic surface control technique for a class of semistrict feedback nonlinear systems with bounded external disturbances. Neural networks (NNs) are introduced as approximators for uncertain nonlinearities and the dynamic surface control (DSC) technique is involved to solve the so-called “explosion of terms” problem. In addition, the NN is used to approximate the transformed unknown functions but not the original nonlinear functions to overcome the possible singularity problem. The stability of closed-loop system is proven by using Lyapunov function method, and adaptation laws of NN weights are derived from the stability analysis. Finally, a numeric simulation validates the results of theoretical analysis. |
| Key words: Adaptive backstepping Dynamic surface control Semistrict feedback form Radius-basis-function (RBF) networks |
