Adaptive neural control for hysteresis motor driving servo system with Bouc-Wen model
type: Item
instance of: scholarly article
title: Adaptive neural control for hysteresis motor driving servo system with Bouc-Wen model
zbMATH Open document ID: 1398.93156
DOI: 10.1155/2018/9765861
author: Xuehui Gao
published in: Complexity
publication date: 2018-08-08T00:00:00Z
review text: Summary: An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error \(e\) into a scalar error \(s\). Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93C15
Mathematics Subject Classification ID: 93C85
zbMATH DE Number: 6914671
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/9765861
OpenAlex ID: W2884509510
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Wikidata QID: Q129483287
description: scientific article; zbMATH DE number 6914671