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基于UMAC的RBF神经网络PID控制

李明 封航 张延顺

李明, 封航, 张延顺等 . 基于UMAC的RBF神经网络PID控制[J]. 北京航空航天大学学报, 2018, 44(10): 2063-2070. doi: 10.13700/j.bh.1001-5965.2017.0777
引用本文: 李明, 封航, 张延顺等 . 基于UMAC的RBF神经网络PID控制[J]. 北京航空航天大学学报, 2018, 44(10): 2063-2070. doi: 10.13700/j.bh.1001-5965.2017.0777
LI Ming, FENG Hang, ZHANG Yanshunet al. RBF neural network tuning PID control based on UMAC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2063-2070. doi: 10.13700/j.bh.1001-5965.2017.0777(in Chinese)
Citation: LI Ming, FENG Hang, ZHANG Yanshunet al. RBF neural network tuning PID control based on UMAC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2063-2070. doi: 10.13700/j.bh.1001-5965.2017.0777(in Chinese)

基于UMAC的RBF神经网络PID控制

doi: 10.13700/j.bh.1001-5965.2017.0777
基金项目: 

国家自然科学基金 11202010

国家自然科学基金 61473019

详细信息
    作者简介:

    李明  女, 博士, 讲师。主要研究方向:振动分析与控制、惯性导航与组合导航

    封航  男, 硕士研究生。主要研究方向:精密伺服控制、运动控制

    张延顺   男, 博士, 副教授。主要研究方向:惯性导航与组合导航

    通讯作者:

    李明, E-mail:liliyalm@buaa.edu.cn

  • 中图分类号: TP301.6

RBF neural network tuning PID control based on UMAC

Funds: 

National Natural Science Foundation of China 11202010

National Natural Science Foundation of China 61473019

More Information
  • 摘要:

    针对通用电机运动控制器(UMAC)下的传统PID控制和现有的模糊PID控制自适应性和鲁棒性较差,伺服系统的动静态性能不理想的问题,将RBF神经网络引入到UMAC的PID参数调节中,增强伺服系统的自适应性和鲁棒性,并提高系统动静态特性。通过UMAC的嵌入式PLC程序对算法进行了实现,位置阶跃响应实验和正弦跟踪实验表明,RBF神经网络PID控制下的伺服电机位置阶跃响应上升时间由传统PID控制下的0.164 s和模糊PID控制下的0.118 s减小到了0.017 s,峰值时间由传统PID控制下的0.196 s和模糊PID控制下的0.131 s减小到了0.023 s,调节时间由传统PID控制下的0.216 s和模糊PID控制下的0.142 s减小到了0.025 s,电机响应速度变快;RBF神经网络PID控制下的伺服电机位置正弦响应动态跟随最大误差由传统PID控制下的188 counts和模糊PID控制下的120 counts减小到了39 counts,且误差波动较小、平稳,伺服电机动态跟随性能显著提高。

     

  • 图 1  基于UMAC的伺服系统结构

    Figure 1.  Structure of servosystem based on UMAC

    图 2  PMSM传递模型

    Figure 2.  Transfer model of PMSM

    图 3  RBF神经网络结构

    Figure 3.  Structure of RBF neural network

    图 4  RBF神经网络PID控制的PMSM伺服仿真模型

    Figure 4.  Servo simulation model of PMSM based on RBF neural network tuning PID control

    图 5  传统PID控制、模糊PID控制、RBF神经网络PID控制下位置阶跃响应曲线

    Figure 5.  Step response of position by traditional PID control, fuzzy PID control and RBF neural network tuning PID control

    图 6  UMAC的PID算法原理图

    Figure 6.  PID algorithm schematic diagram of UMAC

    图 7  RBF神经网络PID控制PLC程序流程图

    Figure 7.  PLC program flowchart of RBF neural network tuning PID control

    图 8  精密装配系统

    Figure 8.  Precise assembly system

    图 9  基于UMAC的伺服系统

    Figure 9.  Servosystem based on UMAC

    图 10  给定位置条件下位置阶跃响应曲线

    Figure 10.  Position step response curves under given position condition

    图 11  给定位置条件下位置正弦响应曲线

    Figure 11.  Position sinusoidal response curves under given position condition

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出版历程
  • 收稿日期:  2017-12-19
  • 录用日期:  2018-01-19
  • 刊出日期:  2018-10-20

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