基于CMAC的电动负载模拟器的研究
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摘要
为了抑制电动负载模拟器中存在的多余力矩,增强加载系统的非线性抑制能力,提出小脑模型(CMAC)神经网络与经典PID结合的复合控制策略,CMAC网络用于前馈控制,经典PID实现反馈控制,能够快速实时地消除多余力矩对系统的干扰。将存储单元的先前学习次数作为可信度,提出基于可信度分配的权值更新算法,来消除常规CMAC网络的过学习现象。建立了电动负载模拟器的数学模型,并给出了具体的算法流程。经仿真和试验结果表明,该方法具有很强的鲁棒性,有效地抑制了系统的多余力矩,提高了系统的加载精度。
In order to prevent the surplus torque in the electric load simulator(ELS)and improve the ability of prevent the nonlinear,hybrid control based on cerebella model articulation controller(CMAC)neural network and classical PID is proposed,the feedforward control is realized by CMAC while the feedback control is realized by classical PID controller,it is able to eliminate surplus torque.Weight update algorithm based on credibility is proposed,the former learned times of the storage unit is used as credibility in order to eliminate the excess self-learning phenomenon.The ELS's model is established and the detailed control structure is put forward.simulation and experimental results demonstrate that the method has good robustness and can effectively eliminate the surplus torqure,improve the loading precision.
In order to prevent the surplus torque in the electric load simulator(ELS)and improve the ability of prevent the nonlinear,hybrid control based on cerebella model articulation controller(CMAC)neural network and classical PID is proposed,the feedforward control is realized by CMAC while the feedback control is realized by classical PID controller,it is able to eliminate surplus torque.Weight update algorithm based on credibility is proposed,the former learned times of the storage unit is used as credibility in order to eliminate the excess self-learning phenomenon.The ELS's model is established and the detailed control structure is put forward.simulation and experimental results demonstrate that the method has good robustness and can effectively eliminate the surplus torqure,improve the loading precision.
引文
[1]JIAO Z X,GAO J X,HUA Q,et al.The Velocity Synchro-nizing Control on the Electro-Hydraulic Load Simulator[J].Chinese Journal of Aeronautics,2004,17(1):39-46.
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[3]符文星,朱苏朋,孙力.弹簧杆刚度对电动负载模拟器的性能影响研究[J].弹箭与制导学报,2009,29(2):286-288.
[4]LYU S S,LIN H,FAN M D.Electric loading system with multi closed-loop control[J].Electric Machines&Control,2015,19(9):16-22.
[5]任志婷,焦宗夏.小转矩电动式负载模拟器的设计[J].北京航空航天大学学报,2003,29(1):91-94.
[6]王权,李军,戴立.基于BP神经网络的电动伺服加载算法研究[J].工业仪表与自动化装置,2017(2):8-13.
[7]王修岩,刘艳敏,李宗帅.基于RBF神经网络和重复控制PID的电动负载模拟器复合控制[J].电力科学与技术学报,2017,32(1):84-89.
[8]YANG B,HAN H.A CMAC-PD compound torque controller with fast learning capacity and improved output smoothness for electric load simulator[J].International Journal of Control Automation&Systems,2014,12(4):805-812.
[9]刘大磊.道路模拟试验台CMAC与PID复合控制仿真研究[D].杭州:浙江工业大学,2009.
[10]杨鹏,施光林,泮健.基于CMAC+PID复合控制的多缸力加载控制研究[J].液压与气动,2011(2):49-53.
[2]黄勇,孙力,阎杰.连接刚度对电动负载模拟器性能的影响[J].弹箭与制导学报,2004,24(增刊7):170-173.
[3]符文星,朱苏朋,孙力.弹簧杆刚度对电动负载模拟器的性能影响研究[J].弹箭与制导学报,2009,29(2):286-288.
[4]LYU S S,LIN H,FAN M D.Electric loading system with multi closed-loop control[J].Electric Machines&Control,2015,19(9):16-22.
[5]任志婷,焦宗夏.小转矩电动式负载模拟器的设计[J].北京航空航天大学学报,2003,29(1):91-94.
[6]王权,李军,戴立.基于BP神经网络的电动伺服加载算法研究[J].工业仪表与自动化装置,2017(2):8-13.
[7]王修岩,刘艳敏,李宗帅.基于RBF神经网络和重复控制PID的电动负载模拟器复合控制[J].电力科学与技术学报,2017,32(1):84-89.
[8]YANG B,HAN H.A CMAC-PD compound torque controller with fast learning capacity and improved output smoothness for electric load simulator[J].International Journal of Control Automation&Systems,2014,12(4):805-812.
[9]刘大磊.道路模拟试验台CMAC与PID复合控制仿真研究[D].杭州:浙江工业大学,2009.
[10]杨鹏,施光林,泮健.基于CMAC+PID复合控制的多缸力加载控制研究[J].液压与气动,2011(2):49-53.