架空地线巡检机器人多电机同步控制策略
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摘要
采用机器人巡检地线是电力巡检一种十分重要且实用的方式。由于线路及地形的复杂性对地线巡检机器人运行带来很多不确定的外部干扰。这些问题会造成巡检机器人行进电机运行速度不匹配,进而导致地线巡检机器人发生故障。针对三臂式地线巡检机器人运行时主电机需要保持同步运行的问题,以地线巡检机器人三臂的3台主驱动电机作为控制对象,设计了一种RBF-PID控制器。结合电机控制系统的非线性、时变、容易受到负载的扰动等特性,将神经网络与偏差耦合的控制结构相结合提出了一种多电机同步控制策略。通过Simulink仿真平台进行仿真,与传统的控制方法相比,鲁棒性好,收敛速度快,能够有效地克服扰动带来的误差,实现多电机同步控制,并应用于输电线路自主巡线机器人的实际运行中。
Robot ground wire inspection is a very important and practical way of electric power inspection.Because of the complexity of the line and terrain, there are many uncertain external disturbances to the operation of the ground wire inspection robot, which leads to the mismatching of the moving motor speed of the inspection robot and then to the fault of the ground wire inspection robot. Based on the fact that the main motor of the three-arm ground wire inspection robot needs to operate synchronously, an RBF-PID controller is designed with three main drive motors of three arms as the control object. Given the characteristics of the motor control system such as nonlinearity, time-varying and load disturbance by load, a multi-motor synchronous control strategy is proposed by combining the neural network with the control structure of bias coupling.Through simulation in Simulink platform, the proposed method, compared with the traditional control method,has better robustness, faster convergence speed, and can effectively overcome the error caused by disturbance and realize multi-motor synchronous control, and can be applied to the operation of transmission line autonomous inspection robot.
Robot ground wire inspection is a very important and practical way of electric power inspection.Because of the complexity of the line and terrain, there are many uncertain external disturbances to the operation of the ground wire inspection robot, which leads to the mismatching of the moving motor speed of the inspection robot and then to the fault of the ground wire inspection robot. Based on the fact that the main motor of the three-arm ground wire inspection robot needs to operate synchronously, an RBF-PID controller is designed with three main drive motors of three arms as the control object. Given the characteristics of the motor control system such as nonlinearity, time-varying and load disturbance by load, a multi-motor synchronous control strategy is proposed by combining the neural network with the control structure of bias coupling.Through simulation in Simulink platform, the proposed method, compared with the traditional control method,has better robustness, faster convergence speed, and can effectively overcome the error caused by disturbance and realize multi-motor synchronous control, and can be applied to the operation of transmission line autonomous inspection robot.
引文
[1]张羽,关振宏,王涛,等.基于自抗扰控制技术的多电机同步控制[J].电气自动化,2017,39(2):9-11.
[2]毛诗柱,梁志坤.基于滑模控制的多电机速度同步偏差耦合控制[J].包装工程,2018,39(5):153-157.
[3]马文华,张冬梅,刘昕彤,等.多电机速度同步自适应积分滑模控制[J].包装工程,2017,38(15):121-125.
[4]史婷娜,辛雄,夏长亮.采用虚拟电机的改进偏差耦合多电机同步控制[J].中国电机工程学报,2017,37(23):7004-7013.
[5]夏长亮,李莉,谷鑫,等.双永磁电机系统转速同步控制[J].电工技术学报,2017,32(23):1-8.
[6]MAO L L,KAI Z,WANG X D.Variable exponent reaching law sliding mode control of permanent magnet syn chronous motor[J].Electric Machines and Control,2016,20(4):106-111.
[7]邵文强,康尔良.基于RBF神经网络的永磁同步电动机PID控制[J].微特电机,2018,46(11):75-78.
[8]李孟秋,樊铃,任修勇,等.基于RBF神经网络的开关磁阻电机转矩脉动控制[J].电力系统及其自动化学报,2017,29(12):28-34.
[9]佘致廷,杨婷,秦亚胜,等.电动汽车无离合器神经网络自校正PID控制[J].控制工程,2017,24(6):1124-1130.
[10]GAO Z,SUN J H.Application of improved PSO and PIDneural network controller for multi-motor proportion synchronous control system[J].DEStech Transactions on Computer Science and Engineering,2017:217-225.
[11]王新,候风艳.基于改进的PSO-BP神经网络的无刷直流电机控制[J].电子测量技术,2017,40(2)10-14.
[12]耿强,王少炜,周湛清,等.改进型偏差耦合多电机转速同步控制[J].电工技术学报,2019,34(3):474-482.
[13]TIAN Z,LI S,WANG Y,et al.Multi permanent magnet synchronous motor synchronization control based on vari able universe fuzzy PI method[J].Engineering Letters,2015,23(3):180-188.
[14]申超群,杨静.温室温度控制系统的RBF神经网络PID控制[J].控制工程,2017,24(2):361-364.
[15]HAN H G,ZHOU W D,QIAO J F,et al.A direct selfconstructing neural controller design for a class of nonlin ear systems[J].IEEE Transaction on Neuralorks Netwing Learnems Systems,2015,26(6):1312-1322.
[16]肖凡,李光,周鑫林.多连杆机械臂GA-RBF神经网络轨迹跟踪控制[J].机械科学与技术,2018,37(5):669-674.
[17]FEI J T,WO D.Adaptive control of MEMS gyroscope using fully tuned RBF neural network[J].Neural Computing and Applications,2017,28(4):695-702.
[18]DENG J,XU D Z,YAN W X,et al.Adaptive neural network automatic parking constrained control via antiwindup compensator[J].Advances in Mechanical Engineering,2017,9(5):1-10.
[19]赵海波,王承光.双电机驱动伺服系统径向基函数神经网络反推自适应控制[J].控制理论与应用,2018,35(9):1272-1284.
[20]SINHA N K,TIWARI P M.Multiple motor synchronization using nonlinear PID control[C]//2017 3rd International Conference on Advances in Computing,Communication&Automation(ICACCA)(Fall),Dehradun,India.IEEE,2017:1-6.
[2]毛诗柱,梁志坤.基于滑模控制的多电机速度同步偏差耦合控制[J].包装工程,2018,39(5):153-157.
[3]马文华,张冬梅,刘昕彤,等.多电机速度同步自适应积分滑模控制[J].包装工程,2017,38(15):121-125.
[4]史婷娜,辛雄,夏长亮.采用虚拟电机的改进偏差耦合多电机同步控制[J].中国电机工程学报,2017,37(23):7004-7013.
[5]夏长亮,李莉,谷鑫,等.双永磁电机系统转速同步控制[J].电工技术学报,2017,32(23):1-8.
[6]MAO L L,KAI Z,WANG X D.Variable exponent reaching law sliding mode control of permanent magnet syn chronous motor[J].Electric Machines and Control,2016,20(4):106-111.
[7]邵文强,康尔良.基于RBF神经网络的永磁同步电动机PID控制[J].微特电机,2018,46(11):75-78.
[8]李孟秋,樊铃,任修勇,等.基于RBF神经网络的开关磁阻电机转矩脉动控制[J].电力系统及其自动化学报,2017,29(12):28-34.
[9]佘致廷,杨婷,秦亚胜,等.电动汽车无离合器神经网络自校正PID控制[J].控制工程,2017,24(6):1124-1130.
[10]GAO Z,SUN J H.Application of improved PSO and PIDneural network controller for multi-motor proportion synchronous control system[J].DEStech Transactions on Computer Science and Engineering,2017:217-225.
[11]王新,候风艳.基于改进的PSO-BP神经网络的无刷直流电机控制[J].电子测量技术,2017,40(2)10-14.
[12]耿强,王少炜,周湛清,等.改进型偏差耦合多电机转速同步控制[J].电工技术学报,2019,34(3):474-482.
[13]TIAN Z,LI S,WANG Y,et al.Multi permanent magnet synchronous motor synchronization control based on vari able universe fuzzy PI method[J].Engineering Letters,2015,23(3):180-188.
[14]申超群,杨静.温室温度控制系统的RBF神经网络PID控制[J].控制工程,2017,24(2):361-364.
[15]HAN H G,ZHOU W D,QIAO J F,et al.A direct selfconstructing neural controller design for a class of nonlin ear systems[J].IEEE Transaction on Neuralorks Netwing Learnems Systems,2015,26(6):1312-1322.
[16]肖凡,李光,周鑫林.多连杆机械臂GA-RBF神经网络轨迹跟踪控制[J].机械科学与技术,2018,37(5):669-674.
[17]FEI J T,WO D.Adaptive control of MEMS gyroscope using fully tuned RBF neural network[J].Neural Computing and Applications,2017,28(4):695-702.
[18]DENG J,XU D Z,YAN W X,et al.Adaptive neural network automatic parking constrained control via antiwindup compensator[J].Advances in Mechanical Engineering,2017,9(5):1-10.
[19]赵海波,王承光.双电机驱动伺服系统径向基函数神经网络反推自适应控制[J].控制理论与应用,2018,35(9):1272-1284.
[20]SINHA N K,TIWARI P M.Multiple motor synchronization using nonlinear PID control[C]//2017 3rd International Conference on Advances in Computing,Communication&Automation(ICACCA)(Fall),Dehradun,India.IEEE,2017:1-6.