Name
Abstract | ||
|---|---|---|
A new robust controller based on linear-paremeter-neural-networks is designed for a class of nonlinear unkonwn chaotic systems which could be turned to “standard block control type” by using backstepping method. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally and the weights of neural network converge to the optimal weights eventually. The assumption for unknown control function is reduced which stand for the innovation of our method compared with the traditional method. Also the unknown control function needn't to be positive or negative strictly in our paper. This assumption in the other papers is so strict that it couldn't be satisfied by many practical systems. So our method can be applied to a more extensive nonlinear systems. At last, take the unknown Duffing chaotic system for example, simulation study is given to demonstrate that the proposed method is effective. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881070_30 | ICNC |
Keywords | Field | DocType |
null | Lyapunov function,Backstepping,Control theory,Nonlinear system,Control theory,Computer science,Chaotic,Robust control,Bounded function,Step function | Conference |
Volume | Issue | ISSN |
4221 LNCS - I | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-45901-4 | 3 | 0.66 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Wang | 1 | 117 | 28.52 |
| Hongxin Wang | 2 | 3 | 0.66 |
| Hong Li | 3 | 3 | 0.66 |
| Junwei Lei | 4 | 30 | 11.27 |