Fuzzy Neural Network Synchronous Control of H-type Platform Based on Active Disturbance Rejection

doi:10.11985/2022.03.014

Abstract

Abstract:

In order to reduce the influence of parameter perturbation, load disturbance, friction disturbance and other uncertain disturbances on the position synchronization accuracy of dual linear motors in H-type platform, a control method combining active disturbance rejection controller(ADRC) and fuzzy pi-sigma neural network synchronous compensator is proposed. In order to reduce the position tracking error of permanent magnet linear synchronous motor(PMLSM) servo system, active disturbance rejection technology is used to observe the unmodeled disturbance and external disturbance, and these disturbances are regarded as the “total disturbance” of the system, which can be compensated in real time. At the same time, aiming at the mechanical coupling between the two shafts and the dynamic parameter mismatch between the motors of the direct drive H-type platform, the fuzzy pi-sigma neural network synchronous compensator is used to reduce the synchronization error of the direct drive H-type platform. Finally, the simulation results show that the control strategy can effectively improve the tracking accuracy and synchronization accuracy of the direct drive H-type platform, and enhance the anti-interference performance of the system.

Key words: H-type platform, PMLSM, ADRC, fuzzy neural network synchronous compensator, synchronous control

CLC Number:

TM359

WANG Limei, HAO Zhongyang, FANG Xin, ZHANG Kang. Fuzzy Neural Network Synchronous Control of H-type Platform Based on Active Disturbance Rejection[J]. Journal of Electrical Engineering, 2022, 17(3): 122-129.

Figures/Tables 10

References 19

[1]	滕伟, 柳亦兵, 穆海华. 光刻机工作台超精密运动与同步控制[J]. 机械工程学报, 2011, 47(11):185-190.
	TENG Wei, LIU Yibing, MU Haihua. Ultra-precision motion control and synchronization control for stage of lithography[J]. Journal of Mechanical Engineering, 2011, 47(11):185-190.
[2]	罗品奎, 金建新. 双直线电机驱动的H型运动平台同步控制研究[J]. 机械制造与自动化, 2013, 42(6):158-161.
	LUO Pinkui, JIN Jianxin. Research on dual linear motor synchronous control in H-type stage[J]. Mechanical Manufacturing and Automation, 2013, 42(6):158-161.
[3]	CHEN C S, LEE C F, CHOU P H, et al. T-S fuzzy tracking and synchronous control in a gantry stage[C]// IEEE International Conference on Fuzzy Systems,IEEE, 2011:1844-1851.
[4]	张康, 王丽梅. 基于融合误差的直驱H型平台自适应全局滑模轮廓控制[J]. 中国电机工程学报, 2020, 40(16):5337-5345.
	ZHANG Kang, WANG Limei. Adaptive global sliding mode contour control of direct drive H-type platform based on integrated error[J]. Proceedings of the CSEE, 2020, 40(16):5337-5345.
[5]	金鸿雁, 赵希梅. 永磁直线伺服系统递归小波Elman神经网络互补滑模控制[J]. 电机与控制学报, 2019, 23(10):102-109.
	JIN Hongyan, ZHAO Ximei. Wavelet-based Elman neural network complementary sliding mode control for permanent magnet linear servo system[J]. Electric Machines and Control, 2019, 23(10):102-109.
[6]	钱振宇. 圆筒型永磁同步直线电机伺服系统的扰动抑制研究[D]. 南京: 南京航空航天大学, 2019.
	QIAN Zhenyu. Study on disturbance suppression of the tubular permanent magnet synchronous linear motor servo system[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019.
[7]	SIRA-RAMIREZ H, LINARES-FLORES J, GARCIA-RODRIGUEZ C, et al. On the control of the permanent magnet synchronous motor:An active disturbance rejection control approach[J]. IEEE Transactions on Control Systems Technology, 2014, 22(5):2056-2063. doi: 10.1109/TCST.2014.2298238
[8]	邱建琪, 留若宸. 永磁同步电机位置伺服系统改进自抗扰控制[J]. 电机与控制学报, 2019, 23(11):42-50.
	QIU Jianqi, LIU Ruochen. Improved active disturbance rejection control for permanent magnet synchronous motor position servo system[J]. Electric Machines and Control, 2019, 23(11):42-50.
[9]	朱进权, 葛琼璇. 基于自抗扰和负载功率前馈的高速磁悬浮系统PWM整流器控制策略[J]. 电工技术学报, 2021, 36(2):320-329.
	ZHU Jinquan, GE Qiongxuan. Control strategy for PWM rectifier of high-speed maglev based on active disturbance rejection control and load power feed-forward[J]. Transactions of China Electrotechnical Society, 2021, 36(2):320-329.
[10]	孙斌, 王海霞. 永磁同步电机调速系统非线性自抗扰控制器设计与参数整定[J]. 中国电机工程学报, 2020, 40(20):6715-6726.
	SUN Bin, WANG Haixia. Nonlinear active disturbance rejection controller design and tuning for permanent magnet synchronous motor speed control system[J]. Proceedings of the CSEE, 2020, 40(20):6715-6726.
[11]	CHEN S Y, LEE C Y. Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator-science direct[J]. Precision Engineering, 2017, 48:191-202. doi: 10.1016/j.precisioneng.2016.12.002
[12]	王丽梅, 张宗雪. H型精密运动平台交叉耦合模糊PID同步控制[J]. 沈阳工业大学学报, 2018, 40(1):1-5.
	WANG Limei, ZHANG Zongxue. Cross-coupled fuzzy PID synchronous control for H-type precision motion platform[J]. Journal of Shenyang University of Technology, 2018, 40(1):1-5.
[13]	KOREN Y. Cross-coupled biaxial computer control for manufacturing systems[J]. Journal of Dynamic Systems,Measurement,and Control, 1980, 102(4):265-272. doi: 10.1115/1.3149612
[14]	金鸿雁, 赵希梅. 双直线电机伺服系统Elman神经网络互补滑模交叉耦合同步控制[J]. 电工技术学报, 2018, 33(21):4971-4978.
	JIN Hongyan, ZHAO Ximei. Synchronization control of Elman neural network complementary sliding mode and cross-coupled control for dual linear motors servo system[J]. Transactions of China Electrotechnical Society, 2018, 33(21):4971-4978.
[15]	王丽梅, 么红岩, 张康. 直接驱动H型平台鲁棒最优同步控制仿真研究[J]. 系统仿真学报, 2022, 34(3):527-535. doi: 10.16182/j.issn1004731x.joss.20-0831
	WANG Limei, YAO Hongyan, ZHANG Kang. Simulation of robust optimal synchronization control for direct drive H-type motion platform[J]. Journal of System Simulation, 2022, 34(3):527-535. doi: 10.16182/j.issn1004731x.joss.20-0831
[16]	楼成林. 基于自适应神经网络的多电机同步控制策略研究[D]. 杭州: 浙江工业大学, 2019.
	LOU Chenglin. Research on multi-motor synchronization control strategy based on adaptive neural network[D]. Hangzhou: Zhejiang University of Technology, 2019.
[17]	BAS E, GROSAN C, EGRIOGLU E, et al. High order fuzzy time series method based on pi-sigma neural network[J]. Engineering Applications of Artificial Intelligence, 2018, 72:350-356. doi: 10.1016/j.engappai.2018.04.017
[18]	聂永. Pi-Sigma神经网络的学习算法研究[D]. 苏州: 苏州大学, 2008.
	NIE Yong. Research on Pi-Sigma neural networks learning algorithms[D]. Suzhou: Suzhou University, 2008.
[19]	XU Rui, ZHOU Miaolei. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network[J]. Smart Material Structures, 2018, 27(4):045002.