Adaptive neural asymptotic tracking control for a class of stochastic non-strict-feedback switched systems
| Autor: | Jian Wu, Yongbo Sun, Qianjin Zhao, Zheng-Guang Wu |
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| Rok vydání: | 2022 |
| Předmět: |
Scheme (programming language)
Artificial neural network Computer Networks and Communications Computer science Applied Mathematics Tracking (particle physics) Signal Nonlinear system Control and Systems Engineering Control theory Nonlinear filter Bounded function Signal Processing computer computer.programming_language |
| Zdroj: | Journal of the Franklin Institute. 359:1274-1297 |
| ISSN: | 0016-0032 |
| DOI: | 10.1016/j.jfranklin.2021.09.016 |
| Popis: | The adaptive asymptotic tracking control problem for a class of stochastic non-strict-feedback switched nonlinear systems is addressed in this paper. For the unknown continuous functions, some neural networks are used to approximate them online, and the dynamic surface control (DSC) technique is employed to develop the novel adaptive neural control scheme with the nonlinear filter. The proposed controller ensures that all the closed-loop signals remain semiglobally bounded in probability, at the same time, the output signal asymptotically tracks the desired signal in probability. Finally, a simulation is made to examine the effectiveness of the proposed control scheme. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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