基于IGWO-AEKF的永磁同步电机参数辨识

doi:10.16182/j.issn1004731x.joss.25-0634

摘要/Abstract

摘要：

针对传统EKF进行PMSM参数辨识时，精度易受负载改变或电机内部参数突变影响而降低的问题，提出一种基于IGWO优化的自适应互联卡尔曼滤波观测器，构造结合新息与残差的自适应机制，实现过程噪声矩阵与系统噪声矩阵的动态调节，规避工况改变时依赖固定协方差矩阵导致参数辨识精度下降问题。建立多参数互联耦合补偿辨识PMSM模型，减轻测量噪声和参数耦合影响辨识精度。设计基于IGWO优化的初始协方差矩阵策略，利用莱维飞行策略以避免陷入局部最优。在直流电压24 V的PMSM仿真实验结果证明了在改变电参时同时具有快速收敛及高识别精度。

关键词: 自适应扩展卡尔曼滤波, 永磁同步电机, 参数辨识, 改进灰狼算法

Abstract:

The accuracy of the traditional EKF in parameter identification of the PMSM tends to be degraded under load changes or abrupt changes in internal parameters of the motor. This paper proposes an IGWO adaptive interconnected Kalman filter observer, which constructs an adaptive mechanism that combines the innovation and residuals to achieve dynamic adjustment of the process noise matrix and system noise matrix, thereby avoiding the problem of reduced parameter identification accuracy due to reliance on fixed covariance matrices under operating condition changes. A multi-parameter interconnected coupling compensation identification model for PMSM is built to mitigate the effects of measurement noise and parameter coupling on identification accuracy. A strategy for the initial covariance matrix optimized by IGWO is designed, and the Lévy flight strategy is adopted to avoid falling into local optima. Simulation experiments on PMSM with a DC voltage of 24 V verify that both fast convergence and high identification accuracy can be achieved under electrical parameter changes.Keywords: AEKF; PMSM; parameter identification; IGWO

中图分类号:

TM383.4

姚磊,郑子健,李天皓等 . 基于IGWO-AEKF的永磁同步电机参数辨识[J]. 系统仿真学报, 2026, 38(6): 1613-1627.

Yao Lei,Zheng Zijian,Li Tianhao,et al . Parameter Identification of Permanent Magnet Synchronous Motors Based on IGWO-AEKF[J]. Journal of System Simulation, 2026, 38(6): 1613-1627.

图/表 25

图1

图2

表1

适应度函数

基准函数	最优值	标准差(GWO/IGWO)
$f 1 x = ∑ i = 1 n x i 2$	0	4.75×10^-28/9.84×10^-55
$f 2 = = ∑ i = 1 d x i 2 - 10 c o s (2 π x i) + 10]$	0	2.68×10^-14/0
$f 3 = - 20 e x p - 0.2 1 d ∑ i = 1 d x i 2 - e x p 1 d ∑ i = 1 d c o s (2 π x i) + e + 20$	0	9.99×10^-14/1.46×10^-14
$f 4 = = ∑ i = 1 n \| x i \| + ∏ i = 1 d \| x i \|$	0	7.46×10^-17/8.39×10^-33

表1

图3

图4

表2

表3

图5

图6

表4

协方差矩阵

算法	过程噪声协方差矩阵 Q	观测噪声协方差矩阵 R
EKF(AEKF)	$Q 1 = d i a g$ $[10 - 8 10 - 8$ $10 - 8 10 - 8]$ , $Q 2 = d i a g$ $[10 - 8 10 - 8]$	$R 1 = d i a g [11]$ , R₂=1
PSO-AEKF	$Q 1 = d i a g$ [ $6 . 603 - 8 7 . 275 - 8$ $2 . 122 - 5 5 . 493 - 5]$ , $Q 2 = d i a g$ $[3 . 58 - 6 5 . 25 - 6]$	$R 1 = d i a g [48.12 16.58]$ , R₂=0.451
IGWO-AEKF	$Q 1 = d i a g [4 . 285 - 8 1 . 134 - 8$ $2 . 529 - 6 6 . 308 - 6]$ , $Q 2 = d i a g$ $[4 . 58 - 5 9 . 72 - 5]$	$R 1 = d i a g [37.87 18.75]$ , R₂=2.846

表4

图7

图8

图9

表5

表6

图10

表7

图11

表8

图12

图13

图14

图15

图16

表9

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电机参数名称	数值(仿真)	数值(实验)
极对数n_p	4	4
定子电阻R_s/Ω	0.48	0.89
d-q轴电感L_s/mH	8.5	6.5
磁链ψ_f/Wb	0.12	0.2
转动惯量J/(kg·m²)	0.003	0.002 8
电机转速/(r/min)	1 000	1 200

辨识参数		R_s/Ω		ψ_f/Wb		L_s/mH
真实值		0.48		0.12		8.5
负载转矩		5	8	5	8	5	8
EKF	辨识值	0.485 4	0.486 0	0.120 4	0.120 4	8.534 9	8.536 0
EKF	误差/%	1.117	1.231	0.300	0.342	0.411	0.423
AEKF	辨识值	0.481 3	0.481 5	0.120 3	0.120 3	8.518 6	8.519 6
AEKF	误差/%	0.260	0.304	0.207	0.212	0.219	0.231
PSO-AEKF	辨识值	0.481 4	0.481 5	0.119 8	0.120 4	8.485 4	8.486 3
PSO-AEKF	误差/%	0.302	0.304	0.183	0.175	0.171	0.161
IGWO-AEKF	辨识值	0.480 4	0.480 4	0.120 1	0.120 1	8.508 5	8.508 5
IGWO-AEKF	误差/%	0.088	0.089	0.067	0.067	0.100	0.100

算法	单步执行时间/μs	总仿真时间/s
EKF	4.2	2.52
AEKF	5.1(+21.4%)	3.06
PSO-AEKF	15.3(+264%)	9.18
IGWO-AEKF	6.8(+61.9%)	4.08

辨识参数		8.5 mH	9 mH
EKF	辨识值/mH	8.534 9	9.037 2
EKF	误差/%	0.411	0.413
AEKF	辨识值/mH	8.518 6	9.019 9
AEKF	误差/%	0.219	0.221
PSO-AEKF	辨识值/mH	8.485 4	8.984 7
PSO-AEKF	误差/%	0.171	0.170
IGWO-AEKF	辨识值/mH	8.508 5	9.009 0
IGWO-AEKF	误差/%	0.10	0.10

辨识参数		0.12 Wb	0.14 Wb
EKF	辨识值/Wb	0.120 36	0.140 55
EKF	误差/%	0.30	0.393
AEKF	辨识值/Wb	0.120 25	0.140 35
AEKF	误差/%	0.207	0.25
PSO-AEKF	辨识值/Wb	0.119 78	0.139 74
PSO-AEKF	误差/%	0.183	0.185
IGWO-AEKF	辨识值/Wb	0.120 08	0.140 09
IGWO-AEKF	误差/%	0.067	0.066