Adaptive neural network control scheme of switched systems with input saturation
type: Item
instance of: scholarly article
title: Adaptive neural network control scheme of switched systems with input saturation
zbMATH Open document ID: 1459.93185
DOI: 10.1155/2020/7259613
author: Xiaoli Jiang
author: Mingyue Liu
author: Si-Qi Liu
author: Jing Xu
author: Lina Liu
published in: Discrete Dynamics in Nature and Society
publication date: 2020-10-14T00:00:00Z
review text: Summary: This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.
Mathematics Subject Classification ID: 93E15
Mathematics Subject Classification ID: 93D15
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 7260929
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/7259613
OpenAlex ID: W3084314867
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description: scientific article; zbMATH DE number 7260929