• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2024, Vol. 60 ›› Issue (4): 126-133.doi: 10.3901/JME.2024.04.126

• 特邀专栏:智能液压元件及系统基础技术 • 上一篇    下一篇

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基于神经网络的比例伺服阀阀芯液动力补偿鲁棒智能控制

周宁, 姚建勇, 邓文翔   

  1. 南京理工大学机械工程学院 南京 210094
  • 收稿日期:2023-07-08 修回日期:2023-11-10 出版日期:2024-02-20 发布日期:2024-05-25
  • 通讯作者: 姚建勇,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液系统伺服控制、动态系统故障检测与容错、半实物动态仿真技术。E-mail:jerryyao.buaa@gmail.com
  • 作者简介:周宁,女,1998年出生,博士研究生。主要研究方向为机电伺服控制、比例伺服阀阀芯智能控制。E-mail:zhouning98@njust.edu.cn;邓文翔,男,1991年出生,博士,副教授,硕士研究生导师。主要研究方向为机电液系统伺服控制。E-mail:wxdeng_njust@163.com
  • 基金资助:
    国家重点研发计划(2021YYFB2011300); 国家自然科学基金(52075262);国家自然科学基金(52275062); 江苏省研究生科研与实践创新计划(KYCX23_0421)资助项目

Neural Network-based Robust Intelligent Control of Proportional Servo Valve Center with Flow Force Compensation

ZHOU Ning, YAO Jianyong, DENG Wenxiang   

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094
  • Received:2023-07-08 Revised:2023-11-10 Online:2024-02-20 Published:2024-05-25

摘要: 比例伺服阀广泛应用于智能工程机械、国防装备等高端液压系统中。对于智能比例伺服阀而言,液动力是限制其智能化程度提升最主要的因素。针对上述问题,提出一种基于神经网络的阀芯液动力补偿鲁棒控制器(Flow forcecompensationneuralnetwork-basedrobustcontroller,FF-NNRC)。首先利用Fluent软件,获取在不同阀芯位移、压力边界条件下的液动力信息,用于模拟真实工况下的液动力扰动。设计神经网络学习逼近液动力扰动,从而在模型前馈补偿项构建液动力动态补偿项,针对系统其他扰动及神经网络估计误差设计鲁棒项加以克服。Lyapunov稳定性理论证明提出的控制策略可以实现系统的有界稳定。仿真结果表明,与传统的PID控制器和基于名义值模型补偿的鲁棒控制器(Model compensation robust controller,MC-RC)相比,所提出的控制器具有更高的控制精度和抗干扰能力。

关键词: 液动力, 计算流体动力学, 神经网络, 模型补偿, 鲁棒控制

Abstract: Proportional servo valves are widely applied in intelligent engineering machinery, national defence equipment and other high-end hydraulic systems. For the intelligent proportional servo valve, flow force is the most important factor limiting the improvement of its intelligent level. In order to solve the above problems, a flow force compensation neural network-based robust controller(FF-NNRC) of the valve centre is developed. Firstly, Fluent is employed to obtain the flow force information of proportional servo valve under different spool displacements and pressure boundary conditions, which can be used to simulate the flow force disturbance of practical working conditions. Neural network is designed to learn and approximate the flow force disturbance, then handles it in the feedforward model compensation term dynamically, robust term is formulated to deal with other disturbances and neural network estimation error. Lyapunov stability theory proves that the proposed control strategy can achieve the bounded stability of the system. Simulation results show that, compared with traditional PID controller and model compensation robust controller(MC-RC), the proposed controller has higher control accuracy and anti-interference ability.

Key words: flow force, computational fluid dynamics(CFD), neural network, model compensation, robust control

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