基于GMM的高性能微定位工作台驱动系统的研制
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摘要
提高微定位工作台的性能是提升高端制造装备加工性能的重要方式之一,针对现有微定位工作台驱动系统存有驱动力小、行程短、定位精度低等问题,研制了一台以超磁致伸缩材料为核心元件微定位工作台的驱动系统。为提高驱动系统输出位移的建模精度,以Jiles-Atherton磁滞模型为基础,建立该驱动系统的输出位移模型;为提高模型参数的辨识精度,将粒子群算法快速局部搜索性与人工鱼群算法全局收敛性相结合,提出一种高精度的混合优化参数辨识算法;为补偿驱动系统的输出位移误差,引入一种动态递归神经网络前馈-模糊PID反馈控制策略;为提高程序的执行效率,采用了高速DSP芯片开发控制系统;通过研制样机,搭建试验平台进行验证。结果表明:所研制驱动系统的最大位移为30.8μm,最大输出力为292.3 N,定位精度达到0.75μm,最大重复性误差为0.4μm,为研制高性能精密定位装置奠定了理论基础。
Improving the performance of a micro positioning worktable is one of major approaches used to improve the processing performance of high-end manufacturing equipment. Aiming at the problems such as low drive force, short stroke and low positioning accuracy existing in the drive system of existing micro positioning worktable, a drive system of micro positioning worktable taking the giant magnetostrictive material as the core component is developed. In order to improve modeling accuracy of output displacement of the drive system, an output displacement model of the drive system is set up based on the Jiles-Atherton hysteresis model. In addition, in order to improve the identification precision of model parameters, a hybrid optimization parameter identification algorithm which has high accuracy and combines the rapid local search function of particle swarm algorithm and the global convergence of artificial fish swarm algorithm is proposed. And in order to compensate the output displacement error of the drive system, a dynamic recurrent neural network feedforward-fuzzy PID feedback control strategy is introduced. And in order to improve the execution efficiency of the program,a high-speed DSP chip is used to develop the control system. The prototype is built and verified with the experimental platform established. The research results indicate that the maximum displacement of the developed drive system is 30.8 μm, the maximum output force is 292.3 N, the positioning accuracy is 0.75 μm and the maximum repeatability error is 0.4 μm. This result lays a theoretical foundation for development of high-performance precision positioning devices.
Improving the performance of a micro positioning worktable is one of major approaches used to improve the processing performance of high-end manufacturing equipment. Aiming at the problems such as low drive force, short stroke and low positioning accuracy existing in the drive system of existing micro positioning worktable, a drive system of micro positioning worktable taking the giant magnetostrictive material as the core component is developed. In order to improve modeling accuracy of output displacement of the drive system, an output displacement model of the drive system is set up based on the Jiles-Atherton hysteresis model. In addition, in order to improve the identification precision of model parameters, a hybrid optimization parameter identification algorithm which has high accuracy and combines the rapid local search function of particle swarm algorithm and the global convergence of artificial fish swarm algorithm is proposed. And in order to compensate the output displacement error of the drive system, a dynamic recurrent neural network feedforward-fuzzy PID feedback control strategy is introduced. And in order to improve the execution efficiency of the program,a high-speed DSP chip is used to develop the control system. The prototype is built and verified with the experimental platform established. The research results indicate that the maximum displacement of the developed drive system is 30.8 μm, the maximum output force is 292.3 N, the positioning accuracy is 0.75 μm and the maximum repeatability error is 0.4 μm. This result lays a theoretical foundation for development of high-performance precision positioning devices.
引文
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[12]WANG H,CHEN B,LIU K,et al.Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis[J].IEEE transactions on neural networks and learning systems,2014,25(5):947-958.
[13]LIU Y,SHAN J,GABBERT U.Feedback/feedforward control of hysteresis-compensated piezo-electric actuators for high-speed scanning applications[J].Smart Materials and Structure,2014,24(1):015012.
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[15]SABLIK M J,JILES D C.A model for magnetostriction hysteresis[J].J.Appl.Phys.,1988,64(10):5402-5404.
[16]JILES D C,ATHERTON D L.Ferromagnetic hysteresis[J].IEEE Transactions on Magnetics,1983,19(5):2183-2185.
[17]何汉林.GMM动态迟滞特性建模及其参数辨识[D].杭州:杭州电子科技大学,2012.HE Hanlin.GMM dynamic hysteresis characteristics modeling and parameter identification[J].Hangzhou:Hangzhou Dianzi University,2012.
[18]罗德相,周永权,黄华娟.粒子群和人工鱼群混合优化算法[J].计算机与应用化学,2009,26(10):1257-1261.LUO Dexiang,ZHOU Yongquan,HUANG Huajuan.Hybrid optimization algorithm based on particle swarm and artifical fish swarm algorithm[J].Computers and Applied Chemistry,2009,26(10):1257-1261.
[2]国务院.国家中长期科学和技术发展规划纲要(2006-2020)[M].北京:中国法制出版社,2006.State Council.The outline of national program for long-and medium-term scientific and technological development(2006-2020)[M].Beijing:China Legal Publishing House,2006.
[3]王彬,屈稳太,邬义杰,等.超磁致伸缩材料磁滞建模方法国内外研究现状评述[J].功能材料,2013,44(16):2295-2300.WANG Bin,QU Wentai,WU Yijie,et al.Review on hysteretic modeling of giant magnetostrictive materials[J].Journal of Functional Materials,2013,16:2295-2300.
[4]喻曹丰,王传礼,魏本柱,等,超磁致伸缩驱动精密定位平台的动态递归神经网络前馈-PD反馈控制[J].光学精密工程,2015,23(10z):417-424.YU Caofeng,WANG Chuanli,WEI Benzhu,et al.DRNNfeed forward-PD feedback control for precision positioning stage based on giant magnetostrictive actuator[J].Optics and Precision Engineering,2015,23(10z):417-424.
[5]ZHU Yuchuan,LI Yuesong.A hysteresis nonlinear model of giant magnetostrictive transducer[J].Journal of Intelligent Material Systems And Structures,2015,26(16):2242-2255.
[6]YU Caofeng,WANG Chuanli,DENG Haishun,et al.Hysteresis nonlinearity modeling and position control for a precision positioning stage based on a giant magnetostrictive actuator[J].RSC Advances,2016,6(64):59468-59476.
[7]ZHANG Yongfei,ZHOU Youhe.Modification of one-dimension coupled hysteresis model for GMM with the domain flexing function[J].Acta Mechanica Solida Sinica,2014,27(5)461-466.
[8]贾振元,王晓煜,王福吉.超磁致伸缩执行器的动力学参数及磁滞模型参数的辨识方法[J].机械工程学报,2007,43(10):9-13.JIA Zhenyuan,WANG Xiaoyu,WANG Fuji.Identificaton method of giant magnetostrictive transducer’s dynamic parameters and magnetic parameters[J].Chinese Journal of Mechanical Engineering,2007,43(10):9-13.
[9]刘慧芳,贾振元,王福吉,等.超磁致伸缩执行器位移模型的参数辨识[J].机械工程学报,2011,47(15):115-120.LIU Huifang,JIA Zhenyuan,WANG Fuji.Parameter identification of displacement model for giant magnetostrictive actuator[J].Journal of Mechanical Engineering,2011,47(15):115-120.
[10]MENG Aihua,YANG Jianfeng,LI Mingfan,et al.Research on hysteresis compensation control of GMM[J].Nonlinear Dynamics,2016,83(1-2):161-167.
[11]孟爱华,祝甲明,刘成龙,等.基于改进PSO算法的GMA迟滞模型参数辨识[J].控制工程,2014,21(5):735-739.MENG Aihua,ZHU Jiaming,LIU Chenglong,et al.Parameter identification of hysteretsis model for GMAbased on improved PSO Algorithm[J].Control Engineering of China,2014,21(5):735-739.
[12]WANG H,CHEN B,LIU K,et al.Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis[J].IEEE transactions on neural networks and learning systems,2014,25(5):947-958.
[13]LIU Y,SHAN J,GABBERT U.Feedback/feedforward control of hysteresis-compensated piezo-electric actuators for high-speed scanning applications[J].Smart Materials and Structure,2014,24(1):015012.
[14]RUDERMAN M,BERTRAM T.Control of magnetic shape memory actuators using observer-based inverse hysteresis approach[J].IEEE Transactions on Control Systems Technology,2014,22(3):1181-1189.
[15]SABLIK M J,JILES D C.A model for magnetostriction hysteresis[J].J.Appl.Phys.,1988,64(10):5402-5404.
[16]JILES D C,ATHERTON D L.Ferromagnetic hysteresis[J].IEEE Transactions on Magnetics,1983,19(5):2183-2185.
[17]何汉林.GMM动态迟滞特性建模及其参数辨识[D].杭州:杭州电子科技大学,2012.HE Hanlin.GMM dynamic hysteresis characteristics modeling and parameter identification[J].Hangzhou:Hangzhou Dianzi University,2012.
[18]罗德相,周永权,黄华娟.粒子群和人工鱼群混合优化算法[J].计算机与应用化学,2009,26(10):1257-1261.LUO Dexiang,ZHOU Yongquan,HUANG Huajuan.Hybrid optimization algorithm based on particle swarm and artifical fish swarm algorithm[J].Computers and Applied Chemistry,2009,26(10):1257-1261.