A neural-network-based hysteresis model for piezoelectric actuators

被引：8

作者：

Ma, Lianwei ^{[1
]}

Shen, Yu ^{[2
]}

Li, Jinrong ^{[3
]}

机构：

[1] Zhejiang Univ, Ningbo Inst Technol, Sch Informat Sci & Engn, Ningbo 315100, Peoples R China

[2] Zhejiang Univ Sci & Technol, Dept Appl Phys, Hangzhou 310023, Peoples R China

[3] Zhejiang Univ Sci & Technol, Dept Automat, Hangzhou 310023, Peoples R China

来源：

REVIEW OF SCIENTIFIC INSTRUMENTS | 2020年 / 91卷 / 01期

基金：

中国国家自然科学基金;

关键词：

UNCERTAIN NONLINEAR-SYSTEMS; RATE-DEPENDENT HYSTERESIS; OUTPUT-FEEDBACK CONTROL; TRACKING CONTROL; COMPENSATION; MEMORY; IDENTIFICATION;

D O I：

10.1063/1.5121471

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

In this paper, a new neural network based hysteresis model is presented. First of all, a variable-order hysteretic operator (VOHO) is proposed via the characteristics of the motion point trajectory. Based on the VOHO, a basic hysteresis model (BHM) is constructed. Next, the input space is expanded from one-dimension to two-dimension based on the BHM so that the method of neural networks can be used to approximate the mapping between the expanded input space and the output space. Finally, three experiments involved with a piezoelectric actuator were implemented to validate the neural hysteresis model. The results of the experiments suggest that the proposed approach is effective.

引用

页数：9

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