HYSTERESIS MODELING AND CONTROL OF PIEZOELECTRIC ACTUATOR

被引：0

作者：

Wang, Shuai ^{[1
]}

Chen, Zhaobo ^{[1
]}

Jiao, Yinghou ^{[1
]}

Mo, Wenchao ^{[1
]}

Liu, Xiaoxiang ^{[2
]}

机构：

[1] Harbin Inst Technol, Sch Mechatron Engn, Harbin, Heilongjiang, Peoples R China

[2] Beijing Inst Control Engn, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2017, VOL 4B | 2018年

基金：

中国国家自然科学基金;

关键词：

DEPENDENT HYSTERESIS; COMPENSATION; DYNAMICS;

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：

The hysteresis characteristic is a common property of intelligent materials, such as shape memory alloy, giant magnetostrictive material and piezoelectric material. It cannot be neglected when the accuracy requirement is at the range of micro meter or smaller. Therefore, it's essential and important to eliminate the hysteresis with some measures as far as possible. In this paper, an experiment is conducted to obtain the hysteresis characteristic of a piezoelectric actuator (PEA) which is designed and fabricated. The relationship between the output displacement and input voltage is established by combining the RBF neural network (RBFNN) and hysteresis operator. In order to compensate the hysteresis of PEA, an inverse model is built by using RBFNN and an inverse hysteresis operator served as feedforward compensation. Then a PI feedback controller is adopted to eliminate the influence the modeling error of feedforward loop. An experiment based on real time control system is conducted to let the output displacement tracking a desired curve. The test results indicate that the hybrid control system is effective in compensating hysteresis of PEA and makes the output displacement controllable.

引用

页数：8

共 23 条

[21] Modeling and High Dynamic Compensating the Rate-Dependent Hysteresis of Piezoelectric Actuators via a Novel Modified Inverse Preisach Model [J].

Xiao, Shunli ;

Li, Yangmin .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (05) :1549-1557

[22] An RBF neural network approach towards precision motion system with selective sensor fusion [J].

Yang, Rui ;

Er, Poi Voon ;

Wang, Zidong ;

Tan, Kok Kiong .

NEUROCOMPUTING, 2016, 199 :31-39

[23]

YOU H, WASTE MANAGE

← 1 2 3 →