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基于自适应神经网络鲁棒观测器的EHA故障诊断与容错控制

赵杰彦 胡健 姚建勇 周海波 王俊龙 曹萌萌

赵杰彦,胡健,姚建勇,等. 基于自适应神经网络鲁棒观测器的EHA故障诊断与容错控制[J]. 北京航空航天大学学报,2023,49(5):1209-1221 doi: 10.13700/j.bh.1001-5965.2021.0416
引用本文: 赵杰彦,胡健,姚建勇,等. 基于自适应神经网络鲁棒观测器的EHA故障诊断与容错控制[J]. 北京航空航天大学学报,2023,49(5):1209-1221 doi: 10.13700/j.bh.1001-5965.2021.0416
ZHAO J Y,HU J,YAO J Y,et al. EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1209-1221 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0416
Citation: ZHAO J Y,HU J,YAO J Y,et al. EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1209-1221 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0416

基于自适应神经网络鲁棒观测器的EHA故障诊断与容错控制

doi: 10.13700/j.bh.1001-5965.2021.0416
基金项目: 国家自然科学基金(51975294);高性能复杂制造国家重点实验室开放课题基金(Kfkt2019-11);航天伺服驱动与传动技术实验室开放基金(LASAT-2021-0503)
详细信息
    通讯作者:

    E-mail:hujiannjust@163.com

  • 中图分类号: TP273

EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer

Funds: National Natural Science Foundation of China (51975294); Open Project Fund of State Key Laboratory of High Performance Complex Manufacturing (Kfkt2019-11); Open Fund of Laboratory of Aerospace Servo Actuation and Transmission (LASAT-2021-0503)
More Information
  • 摘要:

    针对电静液作动器(EHA)功率密度高、工况复杂、元件集成度高、故障种类多的特点,设计了一种基于自适应神经网络鲁棒观测器的电静液作动器故障诊断与容错控制器。对模型的内部状态提出一种鲁棒观测器进行观测,对液压系统弹性模量等不确定性设计参数自适应率进行估计,对摩擦扰动等非线性设计径向基函数(RBF)神经网络予以逼近。通过前馈补偿的方法对故障和参数不确定性进行补偿,同时针对系统其他扰动设计鲁棒项加以克服。利用Lyapunov稳定性定理证明了所提出的控制器在存在故障的情况下可以实现系统的有界稳定。联合仿真结果表明:相对于传统的比例、积分、微分控制器(PID)和自适应鲁棒控制器(ARC),所提出的控制器具有更高的控制精度与鲁棒性。

     

  • 图 1  EHA系统结构

    Figure 1.  Structure of EHA system

    图 2  RBF神经网络结构

    Figure 2.  Structure of RBF neural network

    图 3  控制器结构

    Figure 3.  Structure of controller

    图 4  各控制器跟踪误差曲线

    Figure 4.  Tracking error curves of each controller

    图 5  状态x1估计值及估计误差

    Figure 5.  Estimation value and error of state x1

    图 6  状态x2估计值及估计误差

    Figure 6.  Estimation value and error of state x2

    图 7  状态x3估计值及估计误差

    Figure 7.  Estimation value and error of state x3

    图 8  故障fx估计值及估计误差、残差

    Figure 8.  Estimation value, and error and residual of fault fx

    图 9  参数与非线性项估计曲线

    Figure 9.  Estimation of parameter and nonlinear term

    图 10  AMESim仿真平台搭建的EHA模型[25]

    Figure 10.  EHA model based on AMESim simulation platform[25]

    图 11  AMESim/Simulink联合仿真模型框图

    Figure 11.  Block diagram of AMESim/Simulink co-simulation model

    图 12  工况1下各控制器跟踪误差

    Figure 12.  Tracking error of each controller under ideal working condition

    图 13  工况2下各控制器跟踪误差

    Figure 13.  Tracking error of each controller under pipeline blockage condition

    图 14  工况3下各控制器跟踪误差

    Figure 14.  Tracking error of each controller under internal leakage condition

    图 15  工况4下各控制器跟踪误差

    Figure 15.  Tracking error of each controller under oil contamination condition

    表  1  正弦信号工况下的性能指标

    Table  1.   Performance index under sinusoidal signal condition

    控制器$M_{{\rm{e}}}$$ \mu $$ \sigma $
    PID0.008 9483.456×10−50.004 752
    ARC2.0×10−60.00012343.862×10−5
    BP-FTARC1.4×10−72.079×10−99.09×10−8
    RBF-FTARC8.355×10−88.619×10−106.033×10−8
    下载: 导出CSV

    表  2  联合仿真系统参数

    Table  2.   Parameter of co-simulation system

    参数数值
    液压缸活塞和负载总质量$M$/kg30
    黏滞阻尼系数$B$/(N·(m·s−1)−1)400
    液压缸活塞面积$A$/${{\rm{m}}^2}$904.78×10−6
    液压管路和油缸平均容积${V_{\rm{a} } }/{ { {\rm{m} }^{3} } }$398.1×10−7
    等效弹性模量${\beta _{\rm{e}}}/ {{\rm{MPa}}}$700×106
    泄漏系数$/({ {\rm{L·(min·MPa)} }^{-1} } )$1×10−12
    泵排量${D_{\rm{p} } }/( { { {\rm{cm} }^{ 3} }\cdot{\rm{r}^{-1} } } )$19×10−6
    下载: 导出CSV

    表  3  工况1下各控制器性能指标

    Table  3.   Performance index of each controller under condition 1

    控制器$M_{{\rm{e}}}$$ \mu $$ \boldsymbol{\sigma} $
    PID0.008 3990.000 190 50.005 632
    ARC0.001 3346.89×10−60.000 319
    BP-FTARC0.000 562 32.835×10−60.000 336
    RBF-FTARC0.000 409 32.625×10−60.000 176
    下载: 导出CSV

    表  4  工况2下各控制器性能指标

    Table  4.   Performance index of each controller under condition 2

    控制器$M_{{\rm{e}}}$$ \mu $$ \boldsymbol{\sigma} $
    PID0.013625.979×10−50.008 244
    ARC0.017633.603×10−60.002 419
    BP-FTARC0.004 6232.364×10−60.001 556
    RBF-FTARC0.001 9972.187×10−60.001 126
    下载: 导出CSV

    表  5  工况3下各控制器性能指标

    Table  5.   Performance index of each controller under condition 3

    控制器$M_{{\rm{e}}}$$ \mu $$ \sigma $
    PID0.034 580.000 493 50.01048
    ARC0.037 86−3.829×10−60.003 736
    BP-FTARC0.006 982−1.75×10−60.002 349
    RBF-FTARC0.003 9729.379×10−70.000 638 2
    下载: 导出CSV

    表  6  工况4下各控制器性能指标

    Table  6.   Performance index of each controller under condition 3

    控制器$M_{{\rm{e}}}$$ \mu $$ \sigma $
    PID0.023 415.949×10−50.007 908
    ARC0.031 19−3.605×10−60.000 855 2
    BP-FTARC0.007 8681.759×10−60.002 65
    RBF-FTARC0.003 789−9.339×10−70.000 650 2
    下载: 导出CSV
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  • 收稿日期:  2021-07-26
  • 录用日期:  2021-11-14
  • 网络出版日期:  2021-11-22
  • 整期出版日期:  2023-05-31

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