Neural network adaptive tracking control for a class of uncertain switched nonlinear systems
| Autor: | Mao Wang, Qitian Yin, Guanghui Sun, Xiaolei Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Lyapunov stability
Scheme (programming language) 0209 industrial biotechnology Adaptive control Artificial neural network Computer science Cognitive Neuroscience 02 engineering and technology Computer Science Applications Nonlinear system 020901 industrial engineering & automation Artificial Intelligence Control theory Norm (mathematics) Backstepping 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer computer.programming_language |
| Zdroj: | Neurocomputing. 301:1-10 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2018.01.047 |
| Popis: | The paper is concerned with the tracking control problem of the switched nonlinear systems under arbitrary switchings. Multilayer neural networks are used as a tool for modeling nonlinear functions up to a small error tolerance. In order to improve the tracking performance, through the expansion of the traditional neural network controller design, a multilayer neural network adaptive controller with multilayer weight norm adaptive estimation has been designed. Further, the weight norm adaptive laws are not only used to approximate the first layer network but also every layers. The adaptive updated laws of the controller have been derived from the Lyapunove function method, and the adaptive neural network control schemes have been developed to achieve more smaller semi-global ultimate boundedness of all the signals in the closed-loop than ever before. Finally, the simulation examples of two theorems are given to illustrate the effectiveness of the proposed control scheme separately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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