光学与光电技术, 2020, 18 (1): 91, 网络出版: 2020-08-08  

一种PFSM动态迟滞非线性特征的建模方法

Modeling Method for Dynamic Hysteresis Nonlinear Characteristics of PFSM
作者单位
华中光电技术研究所-武汉光电国家研究中心, 湖北 武汉 430223
摘要
分析了PFSM的迟滞非线性特性和蠕变特性对控制精度的影响, 提出将XGBoost模型应用于PFSM的动态迟滞非线性特征建模。通过提取PFSM的加压历史特征, 有效地解决了迟滞非线性中数据一对多的问题, 提取的特征包括电压的电压值、变化频率、变化方向、电压增量、历史局部极大值与局部极小值和时间间隔等。仿真结果表明, 此模型可以对迟滞非线性特性和蠕变特性有较好的拟合效果, 相对于静态PI模型, 拟合精度提高了约40 μrand。同时具有模型参数容易识别、易移植到嵌入式芯片等优点。该动态迟滞非线性特征建模方法有理论意义和实用价值。
Abstract
The effects of hysteresis nonlinear characteristics and creep characteristics of PFSM on control accuracy are analyzed. The XGBoost model is applied to the dynamic hysteresis nonlinear feature modeling of PFSM. By extracting the voltage history characteristics of PFSM, the problem of one-to-many data in hysteresis nonlinearity is effectively solved. The extracted features include voltage of voltage value, frequency of change, direction of change, voltage increment, historical local maximum and local minimum values and time intervals, etc. The simulation results show that the model has good fitting effect on hysteresis nonlinearity and creep characteristics, and the fitting precision is increased by about 40 μrand compared with the static PI model. At the same time, it has the advantages of easy identification of model parameters and easy transplantation to embedded chip. The dynamic hysteresis nonlinear feature modeling method has theoretical significance and practical value.
参考文献

[1] 丁捷, 孙伟, 张智杰. 红外热像仪用扫描振镜伺服控制电路的设计[J]. 光学与光电技术, 2017, 16(5): 79-83. DING Jie, SUN Wei, ZHANG Zhi-jie. Design of scanning galvanometer servo control circuit for infrared thermal imager[J]. Optics and Optoelectronic Technology, 2017, 16(5): 79-83.

[2] 赵广义, 王伟国, 李博. 压电陶瓷迟滞逆模型的前馈PID控制[J]. 压电与声光, 2014, 36(6): 914-916. ZHAO Guang-yi, WANG Wei-guo, LI Bo. Feedforward PID control of piezoelectric ceramic hysteresis inverse model[J]. Piezoelectrics & Sound, 2014, 36(6): 914-916.

[3] Janaideh M A, Pavel Krejcí. Prandtl-Ishlinskii hysteresis models for complex time dependent hysteresis nonlinearities[J]. Physica B: Condensed Matter, 2012, 407(9): 1365-1367.

[4] Sang-Joo Kim, Chang-Hoan Lee. Creep behavior of soft and hard PZT ceramics during mechanical loading and unloading[J]. Journal of the European Ceramic Society, 2015, 35(14): 3827- 3834.

[5] 范伟, 余晓芬. 压电陶瓷驱动器蠕变特性的研究[J]. 仪器仪表学报, 2006, 27(11): 1383-1386. FAN Wei, YU Xiao-fen. Research on creep characteristics of piezoelectric ceramic actuators[J]. Journal of Scientific Instrument, 2006, 27(11): 1383-1386.

[6] 范伟, 林瑜阳, 李钟慎. 遗传算法优化的BP神经网络压电陶瓷蠕变预测[J]. 电机与控 制学报, 2018, 22(7): 95-100. FAN Wei, LIN Yu-yang, LI Zhong-shen. Creep prediction of piezoelectric ceramics based on BP neural network optimized by genetic algorithm[J]. Journal of Electric Machinery and Control, 2018, 22(7): 95-100.

[7] Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System[C]. the 22nd ACM SIGKDD International Conference, 2016.

[8] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016. ZHOU Zhi-hua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016.

[9] Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C]. Congress on Evolutionary Computation. IEEE, 2002.

[10] Liu Y, Shan J, Qi N. Creep modeling and identification for piezoelectric actuators based on fractional-order system[J]. Mechatronics, 2013, 23(7): 840-847.

[11] 张新良, 贾丽杰, 付陈琳. 神经网络NARX压电陶瓷执行器迟滞建模[J]. 控制工程, 2019, 26(5): 806-811. ZHANG Xin-liang, JIA Li-jie, FU Chen-lin. Hysteresis modeling of neural network NARX piezoceramic actuator[J]. Control Engineering, 2019, 26(5): 806-811.

刘国磊, 李君波. 一种PFSM动态迟滞非线性特征的建模方法[J]. 光学与光电技术, 2020, 18(1): 91. LIU Guo-lei, LI Jun-bo. Modeling Method for Dynamic Hysteresis Nonlinear Characteristics of PFSM[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2020, 18(1): 91.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!