A Random Error Compensation Method of MEMS Gyroscope Based on BP Neural Network Combined with PSO-Optimized Kalman Filter

doi:10.12382/bgxb.2022.0110

Abstract

Abstract:

To deal with the large random error of the micro-electro-mechanical-system (MEMS) gyroscope that affects its accuracy, an error compensation method based on BP neural network combined with Quantum-behaved Particle Swarm Optimization (QPSO)-optimized Kalman Filter (KF) is proposed. First, the MEMS gyroscope and turntable data are collected as samples, and the BP neural network is employed for training to establish the error model; then the error of the MEMS gyroscope is compensated by the model; finally, the QPSO algorithm is used to optimize KF to achieve better noise reduction effect. The experimental results show that compared with other methods like BP-KF, QPSO-KF and VMD-WTD, this method has better denoising effect, and the MAE and MSE values of the denoised data are smaller.

Key words: micro-electro-mechanical-system gyroscope, BP neural network, quantum-behaved particle swarm optimization, Kalman filter

CLC Number:

V241.5

WAN Xinwei, WANG Jing, YANG Hui, LI Yi, ZHANG Yuanzai, WANG Lu. A Random Error Compensation Method of MEMS Gyroscope Based on BP Neural Network Combined with PSO-Optimized Kalman Filter[J]. Acta Armamentarii, 2023, 44(2): 556-565.

Figures/Tables 10

References 23

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隐含层节点数	网络学习MSE
2	0.02438
4	0.01924
6	0.01015
8	0.00198
10	0.00178
20	0.00175
40	0.00175
60	0.00174

隐含层节点数	网络学习MSE
2	0.02438
4	0.01924
6	0.01015
8	0.00198
10	0.00178
20	0.00175
40	0.00175
60	0.00174

转速/((°)·s^-1)	Q	R	MSE
1.5	0.0015	6.3651	4.9634×10^-7
-1.5	0.0024	5.7802	4.859×10^-7
3	0.001	6.7147	2.8848×10^-8
-3	0.00201	5.3647	1.1947×10^-7
6	0.0018	5.3162	2.8958×10^-6
-6	0.0012	6.1819	3.6192×10^-6

转速/((°)·s^-1)	Q	R	MSE
1.5	0.0015	6.3651	4.9634×10^-7
-1.5	0.0024	5.7802	4.859×10^-7
3	0.001	6.7147	2.8848×10^-8
-3	0.00201	5.3647	1.1947×10^-7
6	0.0018	5.3162	2.8958×10^-6
-6	0.0012	6.1819	3.6192×10^-6

角速度/((°)·s^-1)	评价指标	原始数据	本文方法	BP-KF	QPSO-KF	VMD-WTD
1.5	MAE	0.1336	6.98×10^-4	4.64×10^-2	3.98×10^-2	4.34×10^-2
	MSE	2.80×10^-2	4.96×10^-7	3.20×10^-3	1.90×10^-3	2.70×10^-3
-1.5	MAE	0.1263	6.87×10^-4	4.01×10^-2	3.35×10^-2	3.69×10^-2
	MSE	2.48×10^-2	4.86×10^-7	2.50×10^-3	1.60×10^-3	2.00×10^-3
3	MAE	0.1327	1.20×10^-4	4.59×10^-2	4.00×10^-2	4.32×10^-2
	MSE	2.75×10^-2	2.89×10^-8	3.10×10^-3	1.90×10^-3	2.60×10^-3
-3	MAE	0.1292	3.30×10^-4	3.52×10^-2	2.57×10^-2	3.12×10^-2
	MSE	2.59×10^-2	1.19×10^-7	1.90×10^-3	9.55×10^-4	1.50×10^-3
6	MAE	0.1265	1.70×10^-3	3.90×10^-2	3.10×10^-2	3.57×10^-2
	MSE	2.51×10^-2	2.90×10^-6	2.30×10^-3	1.30×10^-3	1.90×10^-3
-6	MAE	0.1283	1.90×10^-3	3.91×10^-2	2.98×10^-2	3.57×10^-2
	MSE	2.59×10^-2	3.62×10^-6	2.30×10^-3	1.30×10^-3	1.90×10^-3