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| A Voice Coil Motor-Driven Precision Positioning System Based on Self-Learning Nonlinear PID |
| Cheng Miaomiao1, Zhai Penghui1, Zhang Yingjie2, Li Jian2, Feng Kai2 |
1. School of Electrical and Information Engineering Hunan University Changsha 410082 China;
2. School of Mechanical and Vehicle Engineering Hunan University Changsha 410082 China |
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Abstract As a typical precise-positioning and long-stroke motion system, the precision macro-micro air-floating motion table has been taking more and more research interest due to its free-of-contact friction characteristics. Voice coil motor (VCM) has been proposed as the main driving element for the micro motion table due to its fast response and non-contact feed drives. However, due to the mover suspending to the stator, VCM is susceptible to interference such as load disturbance, vibration, and noise. Some solutions have been proposed in the existing research to eliminate the disturbances and improve positioning accuracy. However, either limited effect or complex implementation problems exist.
Therefore, this paper proposes a novel self-learning nonlinear PID control method to improve the positioning accuracy and robustness of the VCM precision positioning system. The main idea of the proposed control method is to construct a nonlinear PID control law based on the arcsine function, and apply the neural network algorithm to adaptively adjust the weight parameters of the proposed nonlinear function with the time-varying system errors. Accordingly, the pre-designed PID control law assures the PID control parameters change reasonably by following the proposed arcsine function. The BP neural network can provide control flexibility, thus improving the model robustness and control accuracy. Therefore, the proposed self-learning nonlinear PID control is the potential for nonlinear, multivariable, and interference-susceptible systems, such as the precision macro-micro air-floating motion system.
The unit step response experiment is first performed on the micromotion table. The experimental results show that the overshoot of the traditional PID controller is about 8 %, and the steady-state positioning accuracy is 1 μm. The steady-state positioning accuracy of the self-learning nonlinear PID controller is 0.5 μm, and there is no overshoot. The proposed method provides better positioning accuracy and transient response. Some experiments are further carried out with varying load conditions. According to the experimental results, the traditional PID controller presents a greatly increased overshoot and settling time, while the proposed self-learning nonlinear PID presents the same control performance as the no-load case. The proposed method improves the robustness of the VCM-driven micromotion table.
Finally, the long-stroke positioning experiment is performed on the macro-micro motion stable. The micro-motion stable adopts the proposed self-learning nonlinear PID control, while the macro-motion stable adopts the traditional linear PID control. The results show that the macro-motion stable follows the micro motion stable with a fast response. Besides, the positioning accuracy is within 0.5 μm, consistent with the short-stroke positioning experimental results. That proves the decoupled control characteristics of the macro/micro-motion stable. The positioning accuracy of the system is determined by the control performance of the micro-motion stable.
Concluded above, a novel self-learning nonlinear PID controller is proposed for the VCM-driven micro motion stable. It provides with better positioning accuracy, fast transient response and enhanced robustness according to the experimental results. Both short-stroke and long-stroke positioning experiments are carried out. The results verify that the micro motion table is mechanically decoupled from the macro motion table. On the basis of this, the proposed self-learning nonlinear PID and the traditional PID are proposed to be used for the micro/macro motion table respectively. Finally, a reduced control complexity and improved control performance could be achieved for the precision macro-micro air-floating motion table.
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Received: 19 July 2022
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[1] Kurisaki Y, Sawano H, Yoshioka H, et al.A newly developed X-Y planar nano-motion table system with large travel ranges[J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2010, 4(5): 976-984.
[2] 蒋毅, 朱煜, 杨开明, 等. 超精密六自由度微动台耦合动力学建模及分析[J]. 中国电机工程学报, 2014, 34(30): 5451-5457.
Jiang Yi, Zhu Yu, Yang Kaiming, et al.Coupled dynamic modeling and analysis of ultra-precision 6-DOF fine stage[J]. Proceedings of the CSEE, 2014, 34(30): 5451-5457.
[3] 寇宝泉, 张赫. 多自由度短行程超精密平面电机技术发展综述[J]. 电工技术学报, 2013, 28(7): 1-11.
Kou Baoquan, Zhang He.Development of multi-DOF micro-motion ultra-precision planar motors[J]. Transa- ctions of China Electrotechnical Society, 2013, 28(7): 1-11.
[4] Shinno H, Hashizume H.High speed nanometer positioning using a hybrid linear motor[J]. CIRP Annals, 2001, 50(1): 243-246.
[5] Kim K, Choi Y M, Nam B U, et al.Dual servo stage without mechanical coupling for process of manu- facture and inspection of flat panel displays via modular design approach[J]. International Journal of Precision Engineering and Manufacturing, 2012, 13(3): 407-412.
[6] Li Liyi, Chen Qiming, Tan Guangjun, et al.High precision position control of voice coil motor based on single neuron PID[C]//Eighth International Sym- posium on Precision Engineering Measurement and Instrumentation, Chengdu, 2013: 195-202.
[7] 王永龙, 刘品宽. 阵列式音圈电机定位控制系统的设计[J]. 微电机, 2020, 53(5): 25-30.
Wang Yonglong, Liu Pinkuan.Design of control system for voice coil motor arrays[J]. Micromotors, 2020, 53(5): 25-30.
[8] 柴嘉伟, 贵献国. 音圈电机结构优化及应用综述[J]. 电工技术学报, 2021, 36(6): 1113-1125.
Chai Jiawei, Gui Xianguo.Overview of structure optimization and application of voice coil motor[J]. Transactions of China Electrotechnical Society, 2021, 36(6): 1113-1125.
[9] 张大卫, 杜伟涛, 冯晓梅. 面向芯片封装的高速精密定位平台控制系统设计[J]. 天津大学学报, 2006, 39(9): 1060-1065.
Zhang Dawei, Du Weitao, Feng Xiaomei.Design of motion control system with high speed and high precision positioning platform for IC package[J]. Journal of Tianjin University, 2006, 39(9): 1060-1065.
[10] Feng Zhao, Ming Min, Ling Jie, et al.Fractional delay filter based repetitive control for precision tracking: design and application to a piezoelectric nano- positioning stage[J]. Mechanical Systems and Signal Processing, 2022, 164: 108249.
[11] Liu Haitao, Hu Jinchun, Zhu Yu, et al.Disturbance elimination of ultra-precision micro-motion platform by adaptive inverse approach[C]//2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010: V12-178.
[12] Lin C M, Li H Y.Adaptive dynamic sliding-mode fuzzy CMAC for voice coil motor using asymmetric Gaussian membership function[J]. IEEE Transactions on Industrial Electronics, 2014, 61(10): 5662-5671.
[13] Lin C M, Li H Y.A novel adaptive wavelet fuzzy cerebellar model articulation control system design for voice coil motors[J]. IEEE Transactions on Industrial Electronics, 2012, 59(4): 2024-2033.
[14] 韩京清. 自抗扰控制器及其应用[J]. 控制与决策, 1998, 13(1): 19-23.
Han Jingqing.Auto-disturbances-rejection controller and its applications[J]. Control and Decision, 1998, 13(1): 19-23.
[15] 董顶峰, 黄文新, 卜飞飞, 等. 圆筒型反向式横向磁通直线电机定位力补偿二阶自抗扰控制器位置控制[J]. 电工技术学报, 2021, 36(11): 2365-2373.
Dong Dingfeng, Huang Wenxin, Bu Feifei, et al.Second-order ADRC position control with detent force compensation for tubular reversal transverse flux linear machine[J]. Transactions of China Elec- trotechnical Society, 2021, 36(11): 2365-2373.
[16] 朱良红, 张国强, 李宇欣, 等. 基于级联扩张观测器的永磁电机无传感器自抗扰控制策略[J]. 电工技术学报, 2022, 37(18): 4614-4624.
Zhu Lianghong, Zhang Guoqiang, Li Yuxin, et al.Active disturbance rejection control for position sensorless permanent magnet synchronous motor drives based on cascade extended state observer[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4614-4624.
[17] 陈兴林, 刘川, 刘杨, 等. 精密运动平台宏微控制系统的设计[J]. 中南大学学报(自然科学版), 2013, 44(6): 2318-2323.
Chen Xinglin, Liu Chuan, Liu Yang, et al.Dual-stage actuator control system design for precision motion platform[J]. Journal of Central South University (Science and Technology), 2013, 44(6): 2318-2323.
[18] 李振华, 陈桂, 周磊. 音圈电机位置控制的自抗扰算法研究[J]. 南京工程学院学报(自然科学版), 2019, 17(4): 25-30.
Li Zhenhua, Chen Gui, Zhou Lei.ADRC algorithm for position control of voice coil motor[J]. Journal of Nanjing Institute of Technology (Natural Science Edition), 2019, 17(4): 25-30.
[19] Gao Longlong, Wu Chuan, Zhang Dijia, et al.Research on a high-accuracy and high-pressure pneumatic servo valve with aerostatic bearing for precision control systems[J]. Precision Engineering, 2019, 60: 355-367.
[20] 陈启明. 超精密定位音圈电机驱动控制系统研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
[21] 王天鹤, 赵希梅, 金鸿雁. 基于递归径向基神经网络的永磁直线同步电机智能二阶滑模控制[J]. 电工技术学报, 2021, 36(6): 1229-1237.
Wang Tianhe, Zhao Ximei, Jin Hongyan.Intelligent second-order sliding mode control based on recurrent radial basis function neural network for permanent magnet linear synchronous motor[J]. Transactions of China Electrotechnical Society, 2021, 36(6): 1229-1237.
[22] 闻枫, 荆凡胜, 李强, 等. 基于改进BP神经网络的无线电能传输系统接收线圈参数优化[J]. 电工技术学报, 2021, 36(增刊2): 412-422.
Wen Feng, Jing Fansheng, Li Qiang, et al.Opti- mization on receiver parameters of wireless power transfer system based on improved BP neural network[J]. Transactions of China Electrotechnical Society, 2021, 36(S2): 412-422.
[23] 夏长亮, 修杰. 基于RBF神经网络非线性预测模型的开关磁阻电机自适应PID控制[J]. 中国电机工程学报, 2007, 27(3): 57-62.
Xia Changliang, Xiu Jie.RBF ANN nonlinear prediction model based adaptive PID control of switched reluctance motor[J]. Proceedings of the CSEE, 2007, 27(3): 57-62.
[24] Wang Minmin, An Aimin.An adaptive PID control based on BP neural network for the voltage of MFC[C]//Chinese Automation Congress (CAC), Shanghai, China, 2021: 7040-7045. |
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2023, Vol. 38 
