Name
Title | ||
|---|---|---|
Composite learning sliding mode control of uncertain nonlinear systems with prescribed performance |
Abstract | ||
|---|---|---|
This paper explores the prescribed performance tracking control problem of nonlinear systems with triangular structure. To obtain the desired transient performance and precise estimations of uncertain terms, the techniques of neural network control, sliding mode control and composite learning control are incorporated into the proposed control method. The presented control strategy can ensure the tracking error converges to a prescribed small residual set. Compared with the persistent excitation condition required in the conventional adaptive control, the interval excitation condition needed in the proposed control approach is weak, which guarantees that the radial basis function neural networks approximate the unknown nonlinear terms more accurately. Finally, two simulation examples are exploited to manifest the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3233/JIFS-211310 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
Composite learning, prescribed performance, sliding mode control, neural network approximation, prediction error | Journal | 42 |
Issue | ISSN | Citations |
6 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangming Xue | 1 | 0 | 0.34 |
| Funing Lin | 2 | 0 | 0.34 |
| Heng Liu | 3 | 153 | 27.10 |
| Shenggang Li | 4 | 0 | 0.34 |