Modeling and Control of Dielectric Elastomer Actuator Based on Neural Ordinary Differential Equation and Nonlinear Model Predictive Control

被引：0

作者：

Huang, Peng ^{[1
,2
,3
,4
]}

Wang, Ya-Wu ^{[1
,2
,3
]}

Wu, Jun-Dong ^{[1
,2
,3
]}

Su, Chun-Yi ^{[5
]}

Fukushima, Edwardo-Fumihiko ^{[4
]}

机构：

[1] School of Automation, China University of Geosciences, Wuhan

[2] Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan

[3] Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan

[4] School of Engineering, Tokyo University of Technology, Tokyo

[5] Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, H3G 1M8, QC

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2025年 / 51卷 / 01期

关键词：

Dielectric elastomer actuator (DEA); dynamic modeling; neural ordinary differential equation; nonlinear model predictive control;

D O I：

10.16383/j.aas.c240223

中图分类号：

学科分类号：

摘要：

For challenging problems in modeling and control of dielectric elastomer actuators (DEA), this paper proposed dynamic modeling and tracking control methods for a DEA based on the neural ordinary differential equation (ODE) and nonlinear model predictive control (MPC). First, a dynamic model of the DEA was established based on the neural ODE to describe its complicated dynamics behavior. Then, based on the established dynamic model of the DEA, a nonlinear model predictive controller was designed to realize its tracking control objective. Finally, a series of tracking control experiments were conducted on the built experimental platform. In all experimental results, the motion of the DEA can track the target trajectory well, and all relative root-mean-square-errors are no more than 3.30%, which illustrates the effectiveness of proposed dynamic modeling and tracking control methods. © 2025 Science Press. All rights reserved.

引用

页码：186 / 196

页数：10

共 28 条

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