基于流量脉动补偿的电静液作动器神经网络控制

doi:10.3901/JME.2025.04.355

机械工程学报 ›› 2025, Vol. 61 ›› Issue (4): 355-364.doi: 10.3901/JME.2025.04.355

• 交叉与前沿 • 上一篇

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基于流量脉动补偿的电静液作动器神经网络控制

葛曜文^1,2, 杨晓伟¹, 邓文翔¹, 姚建勇¹

1. 南京理工大学机械工程学院南京 210094;
2. 浙江大学流体动力基础件与机电系统全国重点实验室杭州 310027

收稿日期:2024-04-07 修回日期:2024-09-17 发布日期:2025-04-14
作者简介:葛曜文，男，1992年出生，博士。主要研究方向为电静液作动器系统建模、分析与控制。E-mail：geyaowen2020@163.com
姚建勇(通信作者)，男，1984年出生，博士，教授，博士研究生导师。主要研究方向为机电系统伺服控制、动态系统故障检测与容错、武器系统半实物仿真技术、火箭导弹发射技术。E-mail：jerryyao.buaa@gmail.com
基金资助:
国家重点研发计划(2021YFB2011300)、国家自然科学基金(52305063)、国家资助博士后研究人员计划(GZC20233497)、江苏省卓越博士后计划(2023ZB761)和浙江大学流体动力基础件与机电系统全国重点实验室开放基金(GZKF-202319)资助项目。

Neural Network Control of Electrohydrostatic Actuator Based on Flow Pulsation Compensation

GE Yaowen^1,2, YANG Xiaowei¹, Deng Wenxiang¹, YAO Jianyong¹

1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094;
2. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027

Received:2024-04-07 Revised:2024-09-17 Published:2025-04-14

摘要/Abstract

摘要： 流量脉动是柱塞泵的固有属性，理论而言其是由多个柱塞往复运动所产生，但在实际运行中柱塞在缸孔密封容腔内的一次次挤压和排放油液会积存残余压力，并伴随着泄漏等非线性不确定性，致使实际流量脉动与理论流量脉动之间存在偏差。现有电静液作动器(Electro-hydrostatic actuation，EHA)非线性控制方法研究中，通常对流量脉动进行线性处理并前馈补偿，忽视了泵流量脉动的非线性特征以及偏差效应，进而扩大了匹配不确定性的上界，致使控制器增益系数增大引起噪声放大，限制了电静液作动器系统控制精度的提升。为追求更高的控制精度，提出一种基于流量脉动补偿的电静液作动器神经网络控制方法。首先建立理论流量脉动的非线性范式模型用于前馈补偿，随后通过双通道神经网络实时逼近执行器的摩擦及第二通道干扰、理论与实际流量脉动的残差及第三通道干扰。试验结果表明，所提出的基于流量脉动补偿的电静液作动器神经网络控制和另外两种控制方法相比，跟踪精度提升了65%以上，在降低控制器增益和调参难度的同时，获得了更加优异的控制性能。

关键词: 电静液作动器, 神经网络控制, 流量脉动, 前馈补偿

Abstract: Flow pulsation is an inherent attribute of the plunger pump. Theoretically, it is generated by the reciprocating movement of multiple plungers. However, in actual operation, residual pressure will be accumulated by the extrusion and discharge of oil in the cylinder hole sealing cavity of the plunger, which is accompanied by nonlinear uncertainties such as leakage, resulting in deviation between the actual flow pulsation and the theoretical flow pulsation. In the current study of nonlinear control methods for electrohydrostatic actuators(EHA), the flow pulsation is usually treated linearly and compensated feedforward. The above processing method ignores the nonlinear characteristics and deviation effect of pump flow pulsation, thus enlarges the upper bound of the matching uncertainty, resulting in the increase of the controller gain coefficient and the amplification of noise, which limits the improvement of the control accuracy of the EHA. In order to achieve higher control accuracy, a neural network control method of the EHA based on flow pulsation compensation is proposed. Firstly, a nonlinear normal model of theoretical flow pulsation is established for feedforward compensation. Then the friction of the actuator, the interference of the second channel, the residual of the theoretical and actual flow pulsation and the interference of the third channel are approximated in real time by the two-channel neural network. The experimental results show that compared with two other control methods, the tracking accuracy of the proposed method is improved by more than 65%, and the control performance is better while reducing controller gain and tuning difficulty.

Key words: electro-hydrostatic actuator, neural network control, flow pulsation, feedforward compensation

中图分类号:

TG156

葛曜文, 杨晓伟, 邓文翔, 姚建勇. 基于流量脉动补偿的电静液作动器神经网络控制[J]. 机械工程学报, 2025, 61(4): 355-364.

GE Yaowen, YANG Xiaowei, Deng Wenxiang, YAO Jianyong. Neural Network Control of Electrohydrostatic Actuator Based on Flow Pulsation Compensation[J]. Journal of Mechanical Engineering, 2025, 61(4): 355-364.

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基于流量脉动补偿的电静液作动器神经网络控制

Neural Network Control of Electrohydrostatic Actuator Based on Flow Pulsation Compensation

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