On-line Parameter Identification of Electric Load Simulator Using Improved Recursive Least Squares Method

夏玉香; 李成成; 李跃峰

doi:10.13433/j.cnki.1003-8728.20240073

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On-line Parameter Identification of Electric Load Simulator Using Improved Recursive Least Squares Method

更新时间：2026-04-23

- On-line Parameter Identification of Electric Load Simulator Using Improved Recursive Least Squares Method
- Mechanical Science and Technology for Aerospace Engineering Vol. 45, Issue 3, Pages: 548-556(2026)
- 作者机构：
  
  1. 兰州交通大学机构机电工程学院,兰州,730070
  2. 哈尔滨工业大学机电工程学院,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20240073
  CLC：
- Published：2026
- 稿件说明：
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夏玉香, 李成成, 李跃峰. On-line Parameter Identification of Electric Load Simulator Using Improved Recursive Least Squares Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(3): 548-556.
DOI：

夏玉香, 李成成, 李跃峰. On-line Parameter Identification of Electric Load Simulator Using Improved Recursive Least Squares Method[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(3): 548-556. DOI： 10.13433/j.cnki.1003-8728.20240073.

摘要

针对电动负载模拟器在线参数辨识过程中传感器噪声及较小的遗忘因子可能导致的辨识过程不收敛问题，该文通过在传统最小二乘算法中引入低通滤波器和协方差矩阵重调，提出了一种改进的带遗忘因子递归最小二乘在线参数辨识方法(IFRLS)。首先，建立了电动负载模拟器的加载数学模型，设计了辨识过程中的滤波方法。其次，在传统带遗忘因子最小二乘算法的基础上引入协方差重调，解决了辨识结果不收敛问题。最后，进行在线参数辨识实验和辨识准确性验证实验。实验结果表明：该方法解决了辨识过程不收敛问题，并实现了电动负载模拟器全部参数的在线辨识，得到的辨识模型与真实模型的拟合度达到93.8%。

Abstract

For the purpose of achieving accurate on-line identification of all parameters for the electric load simulator

an improved recursive least squares (IFRLS) online parameter identification method is proposed by introducing a low-pass filter and covariance matrix readjustment to the traditional least squares algorithm. Firstly

the mathematical model of the electric load simulator was established

and the filtering method in identification process was designed. Then

based on the traditional least-squares algorithm with forgetting factor

covariance resetting was introduced to deal with the non-convergence problem. Finally

on-line parameter identification and verification experiments were conducted. The experimental results show that the non-convergence problem in the identification process was solved effectively by using the proposed improved recursive least squares algorithm

and online identification for all parameters of the electric load simulator was realized. The obtained identification model has a fit of 93.8% with the real model.

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