一种改进的电动缸举升伺服机构动力学参数辨识方法

doi:10.3901/JME.2025.07.382

摘要/Abstract

摘要： 针对限定记忆区间的循环最小二乘法(Limited memory interval cyclic least squares method, CLS)对电动缸举升伺服机构的非线性扰动参数辨识不精确的问题，提出了一种基于扰动解耦的限定记忆区间循环最小二乘法(Limited memory interval cyclic least squares method based on disturbance decoupling,DCLS)，用于机构全行程内非线性模型和扰动参数的精确辨识拟合。首先，介绍了机构的完整和简化的动力学模型，以及非线性参数难辨识的原因；然后，分析了CLS辨识法在辨识拟合非线性扰动参数的局限性，并通过基于动平衡的不平衡力矩分离测试方法提出了DCLS辨识法；最后，基于实验平台、激励信号和辨识环路，实现了机构行程范围内的非线性模型和扰动参数精确辨识。实验结果表明：DCLS辨识法辨识系统的速度和位移响应的均方根误差相比于CLS辨识法分别下降了47.6%和48.9%。DCLS辨识法可解决CLS辨识法辨识扰动参数时的互耦合问题，为电动缸举升伺服机构的高精度建模提供参考。

关键词: 电动缸举升机构, 扰动解耦, 记忆区间, 循环最小二乘法

Abstract: Aiming at the problem of inaccurate identification of nonlinear disturbance parameters in electric cylinder(EMA) lifting servo mechanisms using the limited memory interval cyclic least squares(CLS) method, a limited memory interval cyclic least squares method based on disturbance decoupling(DCLS) is proposed. Firstly, the complete and simplified dynamic model of the mechanism, as well as the reasons why nonlinear parameters are difficult to identify, are introduced. Then, the limitations of the CLS identification method in identifying and fitting nonlinear disturbance parameters are analyzed, and the DCLS identification method isproposed through the unbalanced torque separation testing method based on dynamic balance. Finally, based on the experimental platform, excitation signals, and identification loop, precise identification of nonlinear models and disturbance parameters within the mechanism's travel range is achieved. The experimental results show that the root means square errors of the speed and displacement responses identified by the DCLS identification method have decreased by 47.6% and 48.9% respectively compared to the CLS identification method. The DCLS identification method can solve the problem of mutual coupling when identifying disturbance parameters using the CLS identification method, and provide reference for high-precision modeling of EMA servo lifting mechanisms.

Key words: EMA lifting mechanism, disturbance decoupling, memory interval, cyclic least square method

中图分类号:

TP273

万子平, 唐乐为, 任广安, 范大鹏. 一种改进的电动缸举升伺服机构动力学参数辨识方法[J]. 机械工程学报, 2025, 61(7): 382-395.

WAN Ziping, TANG Lewei, REN Guangan, FAN Dapeng. An Improved Parameter Identification Method of EMA Lifting Mechanism[J]. Journal of Mechanical Engineering, 2025, 61(7): 382-395.

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