| 引用本文: | 万子平,范世珣,马丽莎,陈宁,任广安,范大鹏.电动缸举升伺服机构动力学建模与参数辨识[J].控制理论与应用,2024,41(8):1396~1407.[点击复制] |
| WAN Zi-ping,FAN Shi-xun,MA Li-sha,CHEN Ning,REN Guan-gan,FAN Da-peng.Modeling & identification of EMA lifting servo mechanism[J].Control Theory and Technology,2024,41(8):1396~1407.[点击复制] |
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| 电动缸举升伺服机构动力学建模与参数辨识 |
| Modeling & identification of EMA lifting servo mechanism |
| 摘要点击 3527 全文点击 119 投稿时间：2022-08-06 修订日期：2024-04-20 |
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| DOI编号 10.7641/CTA.2023.20704 |
| 2024,41(8):1396-1407 |
| 中文关键词 电动缸举升机构 正交投影定理 记忆区间 循环最小二乘法 |
| 英文关键词 EMA lifting mechanism Orthogonal projection theorem Memory interval Cyclic least square method |
| 基金项目 国家重点研发计划支助项目（2019YFB2004700）,国家自然科学基金支助项目(52105077) |
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| 中文摘要 |
| 针对电动缸举升伺服机构动力学参数辨识困难的问题, 本文提出了一种限定记忆区间的循环最小二乘辨
识法, 用于机构全行程内非线性模型、扰动参数的辨识拟合. 首先, 分析了机构完整模型的非线性参数成因, 并提出
了相应的简化模型; 然后, 分析了最小二乘法在平均位姿下的参数近似辨识特性, 并提出了限定记忆区间的循环最
小二乘法; 最后, 通过构造输入输出信号和选择辨识伺服环路, 使得辨识过程不会超过机构的行程范围. 实验结果表
明: 所提辨识法使得正弦速度和位移响应均方根误差相比最小二乘法分别下降了59.3%和84.2%. 所提辨识法可给
电动缸举升伺服机构的高精度动力学建模, 控制器结合观测器的复合控制策略的实现提供参考. |
| 英文摘要 |
| To solve the problem of difficult identification of dynamic parameters of electric mechanical actuator (EMA)
lifting servo mechanism, a cyclic least square identification method with limited memory interval is proposed, which is
used to identify and fit the nonlinear model and disturbance parameters of the mechanism in the whole stroke. Firstly,
the cause of nonlinear parameters of the complete mechanism model is analyzed, and the corresponding simplified model
is proposed; Then, the parameter approximate identification characteristics of the least squares method under the average
pose are analyzed, and the cyclic least squares method with limited memory interval is proposed; Finally, by constructing
the input and output signals and selecting the identification servo loop, the identification process will not exceed the travel
range of the mechanism. The experimental results show that the proposed identification method makes the root mean square
error of sinusoidal velocity and displacement response reduce by 59.3% and 84.2% respectively compared with the least
square method. The proposed identification method can provide reference for high-precision dynamic modeling of EMA
lifting servo mechanisms and the implementation of composite control strategies of controllers and observers |
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