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Article

System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit

State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instruments, Tsinghua University, Beijing 100084, China
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Academic Editor: Vittorio M. N. Passaro
Sensors 2016, 16(2), 175; https://doi.org/10.3390/s16020175
Received: 23 November 2015 / Revised: 26 January 2016 / Accepted: 27 January 2016 / Published: 29 January 2016
(This article belongs to the Section Physical Sensors)
Errors compensation of micromachined-inertial-measurement-units (MIMU) is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily compensated through a comprehensive calibration. In this paper, we also present a thermal-gas MIMU based on thermal expansion, which measures three-axis angular rates and three-axis accelerations using only three thermal-gas inertial sensors, each of which capably measures one-axis angular rate and one-axis acceleration simultaneously in one chip. The developed MIMU (100 × 100 × 100 mm3) possesses the advantages of simple structure, high shock resistance, and large measuring ranges (three-axes angular rates of ±4000°/s and three-axes accelerations of ±10 g) compared with conventional MIMU, due to using gas medium instead of mechanical proof mass as the key moving and sensing elements. However, the gas MIMU suffers from cross-coupling effects, which corrupt the system accuracy. The proposed compensation method is, therefore, applied to compensate the system errors of the MIMU. Experiments validate the effectiveness of the compensation, and the measurement errors of three-axis angular rates and three-axis accelerations are reduced to less than 1% and 3% of uncompensated errors in the rotation range of ±600°/s and the acceleration range of ±1 g, respectively. View Full-Text
Keywords: micromachined inertial measurement unit; inertial sensor; thermal gas sensor; error compensation; neural network micromachined inertial measurement unit; inertial sensor; thermal gas sensor; error compensation; neural network
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MDPI and ACS Style

Liu, S.Q.; Zhu, R. System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit. Sensors 2016, 16, 175. https://doi.org/10.3390/s16020175

AMA Style

Liu SQ, Zhu R. System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit. Sensors. 2016; 16(2):175. https://doi.org/10.3390/s16020175

Chicago/Turabian Style

Liu, Shi Q., and Rong Zhu. 2016. "System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit" Sensors 16, no. 2: 175. https://doi.org/10.3390/s16020175

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