A Neural-Network-Based Model of Hysteresis in Magnetostrictive Actuators

被引：0

作者：

Shen, Yu ^{[1
]}

Ma, Lianwei ^{[1
]}

Li, Jinrong ^{[1
]}

Zhao, Xinlong ^{[2
]}

Zhang, Xiuyu ^{[3
]}

Zheng, Hui ^{[1
]}

机构：

[1] Zhejiang Univ Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China

[2] Zhejiang Sci Tech Univ, Hangzhou 310018, Zhejiang, Peoples R China

[3] Northeast Dianli Univ, Jilin 132012, Peoples R China

来源：

PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2016年

关键词：

RATE-DEPENDENT HYSTERESIS; SYSTEMS; LASER;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new neural-network-based hysteresis model is presented. First of all, a variable-power hysteretic operator is proposed via the characteristics of the motion point trajectory of hysteresis for magnetostrictive actuators. Based on the variable-power hysteretic operator, a basic hysteresis model is obtained. And then, a two-dimension input space of neural network is constructed based on the basic model, so that neural networks can be used to identify the mapping between the expanded input space and the output space. Finally, two experiments involved with a magnetostrictive actuator were implemented to validate the neural hysteresis model. The results of the experiments suggest that the proposed approach is effective.

引用

页码：1737 / 1741

页数：5

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