压电驱动平台的自适应PID控制
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摘要
为了辨识出最优的PID控制参数,建立压电驱动平台传动系统的动力学模型;根据压电陶瓷实际的迟滞特性曲线的非对称性,改进为非对称的PI迟滞模型,形成基于改进PI迟滞模型的前馈PID控制。采用基于改进的单神经元自适应PID控制算法和基于BP神经网络的PID控制算法,实现PID参数的在线实时调整。搭建压电陶瓷驱动平台实验系统,并进行闭环控制实验。结果显示,驱动平台在前馈PID控制、单神经元自适应PID控制算法及基于BP神经网络自整定PID控制算法下的平均定位误差分别为16.5 nm,8.3 nm及5.1 nm。自适应PID闭环控制精度优于前馈PID控制,神经网络整定PID控制精度高于单神经元自适应PID控制。
In order to identify the optimal PID control parameters,the dynamic model of the piezoelectric drive platform transmission system was established. According to the asymmetry of the actual hysteresis curve of the piezoelectric ceramic,the asymmetric PI delay model was improved,a feed-forward PID control based on improved PI hysteresis model was formed. A way of adjusting the PID parameters online was put forward,which is based on the improved single neuron adaptive PID control algorithm and PID control algorithm of BP neural network. The experimental system of piezoelectric ceramic drive platform was established and carried out. Closed-loop control experiments show that the average positioning error of the drive platform in feed-forward PID control,single neuron adaptive PID control algorithm and PID neural network self-tuning PID control algorithm is 16.5 nm,8.3 nm,and 5.1 nm,respectively. Adapting PID closed loop control accuracy is better than feed-forward PID control,neural network tuning PID control accuracy is higher than single neuron adaptive PID control.
In order to identify the optimal PID control parameters,the dynamic model of the piezoelectric drive platform transmission system was established. According to the asymmetry of the actual hysteresis curve of the piezoelectric ceramic,the asymmetric PI delay model was improved,a feed-forward PID control based on improved PI hysteresis model was formed. A way of adjusting the PID parameters online was put forward,which is based on the improved single neuron adaptive PID control algorithm and PID control algorithm of BP neural network. The experimental system of piezoelectric ceramic drive platform was established and carried out. Closed-loop control experiments show that the average positioning error of the drive platform in feed-forward PID control,single neuron adaptive PID control algorithm and PID neural network self-tuning PID control algorithm is 16.5 nm,8.3 nm,and 5.1 nm,respectively. Adapting PID closed loop control accuracy is better than feed-forward PID control,neural network tuning PID control accuracy is higher than single neuron adaptive PID control.
引文
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[2] DO T N,TJAHJOWIDODO T,LAU M W S,et al.Hysteresis modeling and position control of tendon-sheath mechanism in flexible endoscopic systems[J].Mechatronics,2013,24(1):12-22.
[3]钟博文．基于粘滑驱动的跨尺度精密运动平台及其关键技术研究[D]．哈尔滨:哈尔滨工业大学，2012.
[4] LIU Y,SHAN J,GABBERT U,et al.Hysteresis and creep modeling and compensation for a piezoelectric actuator using a fractional-order Maxwell resistive capacitor approach[J]. Smart Material Structures,2013,22(11):11-20.
[5] XIAO S,LI Y.Modeling and high dynamic compensating the ratedependent hysteresis of piezoelectric actuators via a novel modified inverse preisach model[J]. IEEE Transactions on Control Systems Technology,2013,21(5):1549-1557.
[6]吴伊玲．压电陶瓷两维偏摆镜驱动系统的设计[D]．长春:中国科学院研究生院(长春光学精密机械与物理研究所)，2015.
[7]黄卫清，史小庆，王寅．菱形压电微位移放大机构的设计[J]．光学精密工程，2015,23(3):803-809.
[8] XU Q. Identification and compensation of piezoelectric hysteresis without modeling hysteresis inverse[J]. IEEE Transactions on Industrial Electronics,2013,60(9):3927-3937.
[9]田雷，陈俊杰，崔玉国，等．基于PI迟滞模型的单压电变形镜开环控制[J]．仪器仪表学报，2017,38(1):136-142.
[10]刘金琨．先进PID控制MATLAB仿真．[M]. 2版．北京:电子工业出版社，2004.
[11] SU C Y,OYA M,HONG H.Stable adaptive fuzzy control of nonlinear systems preceded by unknown backlash-like hysteresis[J].IEEE Transactions on Fuzzy Systems,2003,11(1):1-8.
[12]张邦成，韩跃营，王占礼，等．基于遗传算法的按摩机器人手臂控制系统[J]．长春工业大学学报(自然科学版)，2012,33(5):502-507.
[13]赵强，陈云微．基于磁流变技术的车辆座椅悬架减振研究[J]．森林工程，2010,26(6):20-25.