基于最近邻均匀设计神经网络逆模型的电磁标准振动台谐波控制方法研究

doi:10.3901/JME.2023.05.180

机械工程学报 ›› 2023, Vol. 59 ›› Issue (5): 180-191.doi: 10.3901/JME.2023.05.180

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基于最近邻均匀设计神经网络逆模型的电磁标准振动台谐波控制方法研究

张旭飞^1,2, 刘欣超¹, 马杰¹, 张锋阳¹, 权龙^1,2

1. 太原理工大学机械与运载工程学院太原 030024;
2. 新型传感器与智能控制教育部重点实验室太原 030024

收稿日期:2022-08-23 修回日期:2023-01-10 出版日期:2023-03-05 发布日期:2023-04-20
通讯作者: 张旭飞(通信作者),男,1989年出生,博士,副教授,硕士研究生导师。主要研究方向为精密振动计量、测试与控制。E-mail:zhangxufei@tyut.edu.cn
基金资助:
国家自然科学基金(51805360)、山西省基础研究计划(202203021211152)和高端工程机械智能制造国家重点实验室开放基金(HT059-2019)资助项目。

Research on Harmonic Control of Electromagnetic Standard Vibrator Based on Neural Network Inverse Model of Nearest Neighbor Uniform Design

ZHANG Xufei^1,2, LIU Xinchao¹, MA Jie¹, ZHANG Fengyang¹, QUAN Long^1,2

1. College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024;
2. Key Lab of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan 030024

Received:2022-08-23 Revised:2023-01-10 Online:2023-03-05 Published:2023-04-20

摘要/Abstract

摘要： 针对低频电磁标准振动台输出振动受刚度非线性影响而产生严重谐波失真问题，首先基于非线性特性幂级数等效原理，分析了输出振动的加速度失真特性。然后，为克服振动台逆模型辨识样本传统均匀采样法存在的效率低及精度差等问题，提出了最近邻均匀设计输入输出样本提取方法，由均匀设计法在传统采样点中选择小样本数据集，再依据近邻法将不能准确辨识的其他样本点加入数据集，构成最优训练样本集。进一步，辨识得到待控制振动台的神经网络逆模型，将其与原振动台模型串联后构建了谐波失真开环控制系统。最后，仿真及实验分析表明，神经网络控制可将整个工作频段内不同位移幅值振动加速度失真控制在标准要求的2%以内，而提出的最近邻均匀设计样本优化神经网络控制具有更优的谐波失真抑制效果。

关键词: 电磁标准振动台, 逆模型, 谐波控制, 神经网络, 最近邻均匀设计

Abstract: In view of the serious output vibration harmonic distortion caused by the nonlinear stiffness of the low-frequency electromagnetic standard vibrator, the acceleration distortions are analyzed firstly based on the power series equivalent principle of nonlinear characteristics. Then, in order to overcome the low efficiency and poor accuracy of the traditional uniform sampling method for the vibrator inverse model identifying samples, a nearest neighbor uniform design input-output sample extraction method is proposed. The uniform design method selects a small sample data set from the traditional sampling points, and then adds other sample points that cannot be accurately identified to the data set according to the nearest neighbor method to form the optimal training sample set. Further, the neural network inverse model of the vibrator to be controlled is identified, and the harmonic distortion open-loop control system is constructed after it is connected in series with the original vibrator model. Finally, within the whole working frequency band, the simulation and experimental analysis show that the neural network control method can control the vibration acceleration distortion with different displacement amplitudes within 2% of the standard requirements, and the proposed sample optimization neural network control method with nearest neighbor uniform design has better harmonic distortion suppression effect.

Key words: electromagnetic standard vibrator, inverse model, harmonic control, neural network, nearest neighbor uniform design

中图分类号:

TB534

张旭飞, 刘欣超, 马杰, 张锋阳, 权龙. 基于最近邻均匀设计神经网络逆模型的电磁标准振动台谐波控制方法研究[J]. 机械工程学报, 2023, 59(5): 180-191.

ZHANG Xufei, LIU Xinchao, MA Jie, ZHANG Fengyang, QUAN Long. Research on Harmonic Control of Electromagnetic Standard Vibrator Based on Neural Network Inverse Model of Nearest Neighbor Uniform Design[J]. Journal of Mechanical Engineering, 2023, 59(5): 180-191.

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基于最近邻均匀设计神经网络逆模型的电磁标准振动台谐波控制方法研究

Research on Harmonic Control of Electromagnetic Standard Vibrator Based on Neural Network Inverse Model of Nearest Neighbor Uniform Design

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