Name
Title | ||
|---|---|---|
Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form. |
Abstract | ||
|---|---|---|
This paper presents a predictor-based neural dynamic surface control (PNDSC) design method for a class of uncertain nonlinear systems in a strict-feedback form. In contrast to existing NDSC approaches where the tracking errors are commonly used to update neural network weights, a predictor is proposed for every subsystem, and the prediction errors are employed to update the neural adaptation laws.... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2577342 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Nonlinear systems,Transient analysis,Uncertainty,Robustness,Oscillators,Observers,Noise measurement | Nonlinear system,Noise measurement,Computer science,Control theory,Robustness (computer science),System dynamics,Artificial neural network,Observer (quantum physics),Neural adaptation,Strict-feedback form | Journal |
Volume | Issue | ISSN |
28 | 9 | 2162-237X |
Citations | PageRank | References |
24 | 0.61 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhouhua Peng | 1 | 645 | 36.02 |
| Dan Wang | 2 | 714 | 38.64 |
| Jun Wang | 3 | 9228 | 736.82 |