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Micromachines
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MEMS Inertial Device, 2nd Edition
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MEMS Inertial Device, 2nd Edition

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: 30 March 2025 | Viewed by 8862

Special Issue Editor

Prof. Dr. Huiliang Cao

E-Mail Website
Guest Editor
Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 038507, China
Interests: MEMS; gyroscope; extreme environment sensing technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

MEMS inertial devices are the most widely used component of MEMS sensors, including MEMS gyroscopes and MEMS accelerometers. They possess the advantages of small size, light weight, low cost, mass production capacity and good impact resistance. MEMS inertial devices have important application value and broad application prospects in the national economy, national defense and military fields. The development of the current information intelligent era has brought new development opportunities for MEMS inertial devices; thus, MEMS inertial devices have entered a new development stage of higher accuracy and higher reliability. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on (1) microstructure optimization design of MEMS inertial devices, (2) MEMS inertial device measurements and control systems, (3) MEMS inertial device manufacturing technology, and (4) the integrated application of MEMS inertial devices.

We look forward to receiving your submissions!

Prof. Dr. Huiliang Cao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MEMS inertial device
  • MEMS inertial device controlling method
  • inertial device signal processing
  • MEMS inertial device modeling and simulation
  • MEMS gyroscope
  • MEMS accelerometer

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Related Special Issue

Published Papers (5 papers)

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Research

21 pages, 2764 KiB  
Article
Dual Foot-Mounted Localisation Scheme Employing a Minimum-Distance-Constraint Kalman Filter Under Coloured Measurement Noise
by Yuan Xu, Jingwen Yu, Xiangpeng Wang, Teng Li and Mingxu Sun
Micromachines 2024, 15(11), 1346; https://doi.org/10.3390/mi15111346 - 31 Oct 2024
Viewed by 568
Abstract
This study proposes a dual foot-mounted localisation scheme with a minimum-distance-constraint (MDC) Kalman filter (KF) for human localisation under coloured measurement noise (CMN). The dual foot-mounted localisation employs inertial measurement unit (IMUs), one on each foot, and is intended for human navigation. The [...] Read more.
This study proposes a dual foot-mounted localisation scheme with a minimum-distance-constraint (MDC) Kalman filter (KF) for human localisation under coloured measurement noise (CMN). The dual foot-mounted localisation employs inertial measurement unit (IMUs), one on each foot, and is intended for human navigation. The KF under CMN (cKF) is then derived from the data-fusion model of the proposed navigation scheme. Finally, the MDC condition is designed and an MDC–cKF model is proposed to reduce the error in the IMUs. Empirical results showed that the proposed method effectively improves the navigation accuracy from that of MDC–KF, which neglects the effect of CMN. Full article
(This article belongs to the Special Issue MEMS Inertial Device, 2nd Edition)
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18 pages, 3481 KiB  
Article
Research on Adaptive Closed-Loop Control of Microelectromechanical System Gyroscopes under Temperature Disturbance
by Ke Yang, Jianhua Li, Jiajie Yang and Lixin Xu
Micromachines 2024, 15(9), 1102; https://doi.org/10.3390/mi15091102 - 30 Aug 2024
Viewed by 3241
Abstract
Microelectromechanical System (MEMS) gyroscopes are inertial sensors used to measure angular velocity. Due to their small size and low power consumption, MEMS devices are widely employed in consumer electronics and the automotive industry. MEMS gyroscopes typically use closed-loop control systems, which often use [...] Read more.
Microelectromechanical System (MEMS) gyroscopes are inertial sensors used to measure angular velocity. Due to their small size and low power consumption, MEMS devices are widely employed in consumer electronics and the automotive industry. MEMS gyroscopes typically use closed-loop control systems, which often use PID controllers with fixed parameters. These classical PID controllers require a trade-off between overshoot and rise time. However, temperature variations can cause changes in the gyroscope’s parameters, which in turn affect the PID controller’s performance. To address this issue, this paper proposes an adaptive PID controller that adjusts its parameters in response to temperature-induced changes in the gyroscope’s characteristics, based on the error value. A closed-loop control system using the adaptive PID was developed in Simulink and compared with a classical PID controller. The results demonstrate that the adaptive PID controller effectively tracked the changes in the gyroscope’s parameters, reducing overshoot by 96% while maintaining a similar rise time. During gyroscope startup, the adaptive PID controller achieves faster stabilization with a 0.036 s settling time, outperforming the 0.06 s of the conventional PID controller. Full article
(This article belongs to the Special Issue MEMS Inertial Device, 2nd Edition)
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15 pages, 3605 KiB  
Article
Attitude Algorithm of Gyroscope-Free Strapdown Inertial Navigation System Using Kalman Filter
by Xiong Jiang, Tao Liu, Jie Duan and Maosheng Hou
Micromachines 2024, 15(3), 346; https://doi.org/10.3390/mi15030346 - 29 Feb 2024
Cited by 1 | Viewed by 1586
Abstract
A gyroscope-free strapdown inertial navigation system (GFSINS) solves the carrier attitude through the reasonable spatial combination of accelerometers, with a particular focus on the precision of angular velocity calculation. This paper conducts an analysis of a twelve-accelerometer configuration scheme and proposes an angular [...] Read more.
A gyroscope-free strapdown inertial navigation system (GFSINS) solves the carrier attitude through the reasonable spatial combination of accelerometers, with a particular focus on the precision of angular velocity calculation. This paper conducts an analysis of a twelve-accelerometer configuration scheme and proposes an angular velocity fusion algorithm based on the Kalman filter. To address the sign misjudgment issue that may arise when calculating angular velocity using the extraction algorithm, a sliding window correction method is introduced to enhance the accuracy of angular velocity calculation. Additionally, the data from the integral algorithm and the data from the improved extraction algorithm are fused using Kalman filtering to obtain the optimal estimation of angular velocity. Simulation results demonstrate that this algorithm significantly reduces the maximum value and standard deviation of angular velocity error by one order of magnitude compared to existing algorithms. Experimental results indicate that the algorithm’s calculated angle exhibits an average difference of less than 0.5° compared to the angle measured by the laser tracker. This level of accuracy meets the requirements for attitude measurement in the laser scanning projection system. Full article
(This article belongs to the Special Issue MEMS Inertial Device, 2nd Edition)
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17 pages, 7392 KiB  
Article
Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach
by Huijun Zhao, Chong Shen, Huiliang Cao, Xuemei Chen, Chenguang Wang, Haoqian Huang and Jie Li
Micromachines 2024, 15(2), 237; https://doi.org/10.3390/mi15020237 - 2 Feb 2024
Cited by 2 | Viewed by 1278
Abstract
The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC [...] Read more.
The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments. Full article
(This article belongs to the Special Issue MEMS Inertial Device, 2nd Edition)
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15 pages, 4521 KiB  
Article
Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter
by Tianshang Zhao, Chenguang Wang and Chong Shen
Micromachines 2023, 14(10), 1935; https://doi.org/10.3390/mi14101935 - 15 Oct 2023
Cited by 4 | Viewed by 1351
Abstract
To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning [...] Read more.
To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation. Full article
(This article belongs to the Special Issue MEMS Inertial Device, 2nd Edition)
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