线性变参数振动系统的全局辨识

doi:10.3901/JME.2022.23.114

机械工程学报 ›› 2022, Vol. 58 ›› Issue (23): 114-122.doi: 10.3901/JME.2022.23.114

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线性变参数振动系统的全局辨识

蔡宇¹, 刘旭², 程英豪¹

1. 南京航空航天大学机电学院南京 210016;
2. 南京工业大学机械与动力工程学院南京 210009

收稿日期:2022-07-22 修回日期:2022-10-01 出版日期:2022-12-05 发布日期:2023-02-08
通讯作者: 刘旭(通信作者),男,1987年出生,博士,副教授,硕士研究生导师。主要研究方向为智能制造。E-mail:liuxu.smpe@njtech.edu.cn
作者简介:蔡宇,男,1997年出生。主要研究方向为机械系统动力学辨识。E-mail:caiyu2019@nuaa.edu.cn.
基金资助:
国家自然科学基金杰出青年基金资助项目(51925505)。

Global Identification of Linear Parameter-varying Vibration Systems

CAI Yu¹, LIU Xu², CHENG Yinghao¹

1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016;
2. School of Mechanical and Power Engineering, Nanjing Technology University, Nanjing 210009

Received:2022-07-22 Revised:2022-10-01 Online:2022-12-05 Published:2023-02-08

摘要/Abstract

摘要： 制造系统中存在大量振动特性随特定参数变化而变化的线性变参数振动系统。这类线性变参数振动系统的辨识目前主要通过局部辨识方法，为了准确辨识需要在不同调度变量下进行大量实验，往往效率很低。为了准确而高效地辨识线性变参数振动系统，提出一种全局辨识方法。对调度变量连续变化的线性变参数振动系统持续施加激励，将系统的振动微分方程进行时域离散，利用过完备字典函数库对离散模型进行表征，并利用稀疏回归进行求解，即可根据调度变量数据和系统的激励-响应数据一次辨识得到系统模型。以实际机床刀尖结构的模态参数数据，建立线性变参数振动系统代理模型进行验证。在单调度变量和多调度变量案例中，全局辨识得到的模态参数平均误差均在2.7%以下，充分显示了所提出全局辨识方法的有效性，也验证了线性变参数振动系统全局辨识的可行性。

关键词: 线性变参数系统, 振动系统, 系统辨识, 全局辨识, 稀疏回归

Abstract: There are numerous linear parameter-varying vibration systems in manufacturing systems, whose characteristics vary with specific parameters. At present, they are mainly identified through local identification approaches, which cannot balance the identification accuracy and efficiency. In order to identify linear parameter-varying vibration systems accurately and efficiently, a global identification approach is proposed. Excitation should be applied continuously to a linear parameter-varying vibration system whose scheduling variables varies continuously at the same time. The vibration differential equation of the system is discretized in time domain, then the coefficient functions of it are characterized by an over-complete dictionary functions library and solved via sparse regression. And then, the system model can be identified from the data of excitation-response signals and scheduling variables directly. Based on the modal parameters of an actual tool tip structure, the surrogate model of a linear parameter-varying vibration system is established for verification. The average error percentage of modal parameters from global identification in the single-scheduling variable case and the multi-scheduling variables case are both less than 2.7%, which has fully shown the effectiveness of the proposed approach. In addition, the good results in the simulation environment also indicate the feasibility of the global identification for linear parameter-varying vibration systems.

Key words: linear parameter-varying system, vibration system, system identification, global identification, sparse regression

中图分类号:

TH113

蔡宇, 刘旭, 程英豪. 线性变参数振动系统的全局辨识[J]. 机械工程学报, 2022, 58(23): 114-122.

CAI Yu, LIU Xu, CHENG Yinghao. Global Identification of Linear Parameter-varying Vibration Systems[J]. Journal of Mechanical Engineering, 2022, 58(23): 114-122.

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线性变参数振动系统的全局辨识

Global Identification of Linear Parameter-varying Vibration Systems

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