A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filte...

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Autores principales:	Qi Cai, Fanjing Zhao, Qiang Kang, Zhaoqian Luo, Duo Hu, Jiwen Liu, Huiliang Cao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MEMS gyroscope temperature compensation multi-objective particle swarm optimization (MOPSO) variational modal decomposition (VMD) beetle antennae search algorithm (BAS) Elman neural network (Elman NN) Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/4fdd8d7228fe47508bfa0bfa3cb1b060
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spelling	oai:doaj.org-article:4fdd8d7228fe47508bfa0bfa3cb1b0602021-11-25T18:22:48ZA Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope10.3390/mi121112852072-666Xhttps://doaj.org/article/4fdd8d7228fe47508bfa0bfa3cb1b0602021-10-01T00:00:00Zhttps://www.mdpi.com/2072-666X/12/11/1285https://doaj.org/toc/2072-666XTo eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [<i>k</i><sub>best</sub>,<i>a</i><sub>best</sub>] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10<sup>−3</sup>°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.Qi CaiFanjing ZhaoQiang KangZhaoqian LuoDuo HuJiwen LiuHuiliang CaoMDPI AGarticleMEMS gyroscopetemperature compensationmulti-objective particle swarm optimization (MOPSO)variational modal decomposition (VMD)beetle antennae search algorithm (BAS)Elman neural network (Elman NN)Mechanical engineering and machineryTJ1-1570ENMicromachines, Vol 12, Iss 1285, p 1285 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MEMS gyroscope temperature compensation multi-objective particle swarm optimization (MOPSO) variational modal decomposition (VMD) beetle antennae search algorithm (BAS) Elman neural network (Elman NN) Mechanical engineering and machinery TJ1-1570
spellingShingle	MEMS gyroscope temperature compensation multi-objective particle swarm optimization (MOPSO) variational modal decomposition (VMD) beetle antennae search algorithm (BAS) Elman neural network (Elman NN) Mechanical engineering and machinery TJ1-1570 Qi Cai Fanjing Zhao Qiang Kang Zhaoqian Luo Duo Hu Jiwen Liu Huiliang Cao A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
description	To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [<i>k</i><sub>best</sub>,<i>a</i><sub>best</sub>] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10<sup>−3</sup>°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.
format	article
author	Qi Cai Fanjing Zhao Qiang Kang Zhaoqian Luo Duo Hu Jiwen Liu Huiliang Cao
author_facet	Qi Cai Fanjing Zhao Qiang Kang Zhaoqian Luo Duo Hu Jiwen Liu Huiliang Cao
author_sort	Qi Cai
title	A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
title_short	A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
title_full	A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
title_fullStr	A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
title_full_unstemmed	A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope
title_sort	novel parallel processing model for noise reduction and temperature compensation of mems gyroscope
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4fdd8d7228fe47508bfa0bfa3cb1b060
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_version_	1718411255115415552

A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope

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