基于神经网络ODE和非线性MPC的DEA建模与控制

黄鹏; 王亚午; 吴俊东; 苏春翌; 福岛E.文彦

doi:10.16383/j.aas.c240223

基于神经网络ODE和非线性MPC的DEA建模与控制

doi: 10.16383/j.aas.c240223

黄鹏^{1, 2, 3, 4,},
王亚午^{1, 2, 3,},
吴俊东^{1, 2, 3,},
苏春翌^5,,
福岛E.文彦^4,

1.
中国地质大学(武汉)自动化学院武汉 430074 中国
2.
复杂系统先进控制与智能自动化湖北省重点实验室武汉 430074 中国
3.
地球探测智能化技术教育部工程研究中心武汉 430074 中国
4.
东京工科大学工学部东京 192-0982 日本
5.
康考迪亚大学Gina Cody工程与计算机科学学院蒙特利尔 QC H3G 1M8 加拿大

基金项目: 国家自然科学基金面上项目(62273316), 国家建设高水平大学公派研究生项目(202206410064), 高等学校学科创新引智计划项目(111计划)(B17040)资助

详细信息

作者简介:
黄鹏：中国地质大学(武汉)自动化学院博士研究生. 主要研究方向为软体机器人和机器人控制. E-mail: huangpeng@cug.edu.cn

王亚午：中国地质大学(武汉)自动化学院教授. 主要研究方向为机器人控制和非线性系统控制. 本文通信作者. E-mail: wangyawu@cug.edu.cn

吴俊东：中国地质大学(武汉)自动化学院教授. 主要研究方向为软体机器人和非线性系统控制. E-mail: jdwu@cug.edu.cn

苏春翌：加拿大康考迪亚大学Gina Cody工程与计算机科学学院教授. 主要研究方向为机器人控制, 非线性系统控制和软体机器人. E-mail: chun-yi.su@concordia.ca

福岛E.文彦：日本东京工科大学工学部教授. 主要研究方向为机器人和机电一体化. E-mail: fukushimafmhk@stf.teu.ac.jp

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- 被引次数: 0
出版历程
- 收稿日期: 2024-04-24
- 录用日期: 2024-08-14
- 网络出版日期: 2024-09-06

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

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
,

1.
School of Automation, China University of Geosciences, Wuhan 430074, China
2.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3.
Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
4.
School of Engineering, Tokyo University of Technology, Tokyo 192-0982, Japan
5.
Gina Cody School of Engineering and Computer Science, Concordia University, Montreal QC H3G 1M8, Canada

Funds: Supported by General Program of National Natural Science Foundation of China (62273316), Program of China Scholarship Council (202206410064), and Higher Education Discipline Innovation Project of China (111 Project) (B17040)

More Information

Author Bio:
HUANG Peng　Ph.D. candidate at the School of Automation, China University of Geosciences. His research interest covers soft robotics and robot control

WANG Ya-Wu　Professor at the School of Automation, China University of Geosciences. His research interest covers robot control and nonlinear system control. Corresponding author of this paper

WU Jun-Dong　Professor at the School of Automation, China University of Geosciences. His research interest covers soft robotics and nonlinear system control

SU Chun-Yi　Professor at the Gina Cody School of Engineering and Computer Science, Concordia University. His research interest covers robot control, nonlinear system control and soft robotics

FUKUSHIMA Edwardo-Fumihiko　Professor at the School of Engineering, Tokyo University of Technology. His research interest covers robotics and mechatronics

摘要

摘要: 针对介电弹性体驱动器(Dielectric elastomer actuator, DEA)建模与控制的挑战性问题, 提出基于神经网络常微分方程(Ordinary differential equation, ODE)和非线性模型预测控制(Model predictive control, MPC)的DEA动力学建模与跟踪控制方法. 首先, 基于神经网络ODE建立DEA的动力学模型以描述其复杂的动态行为. 然后, 基于所建立的DEA动力学模型, 设计非线性模型预测控制器实现其跟踪控制目标. 最后, 在所搭建的实验平台上进行一系列跟踪控制实验. 在所有实验结果中, DEA的运动均能很好地跟踪目标轨迹, 且相对均方根误差均不超过3.30%, 说明了所提动力学建模与跟踪控制方法的有效性.
- 介电弹性体驱动器 /
- 神经网络常微分方程 /
- 动力学建模 /
- 非线性模型预测控制
Abstract: This paper proposed dynamic modeling and tracking control methods for a dielectric elastomer actuator (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.
- Dielectric elastomer actuator /
- neural ordinary differential equation /
- dynamic modeling /
- nonlinear model predictive control

HTML全文

图 1 锥形介电弹性体驱动器

Fig. 1 Conical dielectric elastomer actuator

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图 2 实验平台

Fig. 2 Experimental platform

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图 3 动力学模型结构框图

Fig. 3 Structure diagram of dynamic model of DEA

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图 4 神经网络结构图

Fig. 4 Structure diagram of neural network

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图 5 损失函数随迭代次数的变化曲线

Fig. 5 Curve of loss function with iterations

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图 6 DEA动力学模型输出值与实验测量值的对比

Fig. 6 Comparison between output of dynamic model of DEA and experimental measurement

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图 7 在不同驱动电压幅值情况下的模型验证结果

Fig. 7 Model validation results in different actuation voltage amplitudes

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图 8 在不同驱动电压频率情况下的模型验证结果

Fig. 8 Model validation results in different actuation voltage frequencies

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图 9 控制系统整体结构框图

Fig. 9 Structure diagram of whole control system

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图 10 对正弦波的跟踪结果

Fig. 10 Tracking result for sinusoidal wave

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图 11 对三角波的跟踪结果

Fig. 11 Tracking result for triangular wave

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图 12 对阶梯波的跟踪结果

Fig. 12 Tracking result for step wave

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图 13 对复合波的跟踪结果

Fig. 13 Tracking result for composite wave

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表 1 第1个实验方案中所有实验的相对均方根误差

Table 1 $E_R$ for all experiments of first experimental scheme

$m$	$a_m$ (kV)	$E_R$
1	5.0	3.12%
2	6.0	1.53%
3	7.0	1.55%
4	8.0	2.16%

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表 2 第2个实验方案中所有实验的相对均方根误差

Table 2 $E_R$ for all experiments of second experimental scheme

$m$	${\psi}_m$ (Hz)	$E_R$
1	0.2	1.82%
2	0.6	2.10%
3	1.0	2.50%
4	1.4	1.89%

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表 3 ${{N}_{p}}$和${{N}_{c}}$取值对控制精度、单步运行时间和实时性的影响

Table 3 Influences of values of ${{N}_{p}}$ and ${{N}_{c}}$ on control accuracy, single-step running time and real-time performance

${{N}_{p}}$	${{N}_{c}}$	$E_R$	$T_c$/s	实时性
2	1	3.26%	$5.08\times {10^{ - 3}}$	满足
4	2	2.83%	$8.39\times {10^{ - 3}}$	满足
5	2	1.88%	$8.91\times {10^{ - 3}}$	满足
6	4	—	$1.85\times {10^{ - 2}}$	不满足

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