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RBF神经网络在静电悬浮位置控制中的应用

陆潇晓 刘晓珂 李虎 郑福 孙志斌 于强

陆潇晓, 刘晓珂, 李虎, 郑福, 孙志斌, 于强. RBF神经网络在静电悬浮位置控制中的应用[J]. 空间科学学报. doi: 10.11728/cjss2022.05.210927103
引用本文: 陆潇晓, 刘晓珂, 李虎, 郑福, 孙志斌, 于强. RBF神经网络在静电悬浮位置控制中的应用[J]. 空间科学学报. doi: 10.11728/cjss2022.05.210927103
LU Xiaoxiao, LIU Xiaoke, LI Hu, ZHENG Fu, SUN Zhibin, YU Qiang. RBF Neural Network in Electrostatic Levitation Position Control (in Chinese). Chinese Journal of Space Science, xxxx, x(x): x-xx doi: 10.11728/cjss2022.05.210927103
Citation: LU Xiaoxiao, LIU Xiaoke, LI Hu, ZHENG Fu, SUN Zhibin, YU Qiang. RBF Neural Network in Electrostatic Levitation Position Control (in Chinese). Chinese Journal of Space Science, xxxx, x(x): x-xx doi: 10.11728/cjss2022.05.210927103

RBF神经网络在静电悬浮位置控制中的应用

doi: 10.11728/cjss2022.05.210927103
基金项目: 中国科学院科研仪器设备研制项目(YJKYYQ20190008)和空间科学战略先导专项(XDA15013600)共同资助
详细信息
    作者简介:

    于强:E-mail:yuqiang@nssc.ac.cn

  • 中图分类号: V524,TP273.2

RBF Neural Network in Electrostatic Levitation Position Control

  • 摘要: 静电悬浮位置控制系统具有非线性、时变性的特点,传统的控制方法不能有效抑制扰动的影响。针对该问题提出了一种神经网络与PID相结合的RBF-PID控制策略。以球形样品为例分析其受力情况,推导出静电悬浮位置控制系统的机理模型。搭建基于RBF-PID控制器的静电悬浮位置控制系统,并根据仿真结果实时在线调整控制参数。仿真结果表明,当样品带电量从10–9 C突变至3×10–9 C时,RBF-PID控制器只需0.12 s即可使样品达到稳定状态。实验结果表明,当样品处于加热状态时,实时调整参数后系统的平均绝对误差为0.0416 mm,控制效果比传统PID控制策略提高了70%。所提出的控制方法辨识精度高,比传统PID方法有着更强的鲁棒性和稳定性。

     

  • 图  1  静电悬浮位置控制系统工作原理

    Figure  1.  Position control system of electrostatic levitation

    图  2  样品位置控制回路

    Figure  2.  Control loop of sample position

    图  3  位置平衡处样品位置与控制电压的关系

    Figure  3.  Relationship between the sample position and the controlling voltage when sample position is in equilibrium

    图  4  RBF神经网络结构

    Figure  4.  Structure diagram of RBF neural network

    图  5  RBF-PID控制器结构回路

    Figure  5.  Structure loop diagram of RBF-PID controller

    图  6  静电悬浮位置控制系统仿真结果

    Figure  6.  Simulation result of position control system of electrostatic levitation

    图  7  各控制器的初始响应曲线

    Figure  7.  Initial response curve of different controllers

    图  8  电荷量突变时各控制器的响应曲线

    Figure  8.  Response curve of different controllers when the charge changes suddenly

    图  9  静电悬浮实验装置

    Figure  9.  Experimental facility of electrostatic levitation

    图  10  样品锆悬浮实验

    Figure  10.  Diagram of sample zirconium in levitation state

    图  11  室温下各控制器的样品位置对比

    Figure  11.  Sample position comparison of different controllers at room temperature

    图  12  加热状态下各控制器的样品位置对比

    Figure  12.  Sample position comparison of different controllers at heating state

    表  1  系统仿真参数

    Table  1.   Parameters of system simulation

    系统参数非线性系统参数值线性系统参数值
    样品质量/mg2626
    上下电极间距/mm1010
    样品带电量/C10–910–9
    学习速率η3070
    动量因子α0.040.09
    参数P学习速率ηp500700
    参数I学习速率ηi850950
    参数D学习速率ηd350490
    下载: 导出CSV

    表  2  仿真结果对比

    Table  2.   Comparison of simulation results

    样品电荷量突变值/C0.5×10–92×10–93×10–9
    最大误差
    /mm
    稳定时间
    /s
    振荡情况最大误差
    /mm
    稳定时间
    /s
    振荡情况最大误差
    /mm
    稳定时间
    /s
    振荡情况
    传统PID0.2478.6剧烈0.1260.242较大0.1700.249较大
    最优整定PID0.1760.2260.0830.1380.1120.173
    RBF-PID0.1740.1180.0790.1100.1250.122
    下载: 导出CSV

    表  3  实验结果对比

    Table  3.   Comparison of experimental results

    室温加热
    误差范围
    /mm
    平均绝对
    误差/mm
    误差范围
    /mm
    平均绝对
    误差/mm
    传统PID–0.130~0.1100.0384–0.430~0.6900.1879
    最优整定PID–0.080~0.1100.0269–0.143~0.2960.0685
    RBF-PID–0.095~0.0800.0172–0.140~0.1850.0416
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-27
  • 录用日期:  2022-03-15
  • 网络出版日期:  2022-09-19

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