Parameter Identification of Permanent Magnet Synchronous Motors Based on IGWO-AEKF

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

Abstract

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

CLC Number:

TM383.4

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

Figures/Tables 25

Fig. 1

Fig. 2

Table 1

Fitness function

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

Table 1

Fig. 3

Fig. 4

Table 2

Table 3

Fig. 5

Fig. 6

Table 4

Covariance matrix

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

Table 4

Fig. 7

Fig. 8

Fig. 9

Table 5

Table 6

Fig. 10

Table 7

Fig. 11

Table 8

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Table 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