A Digital Control Structure for Lissajous Frequency-Modulated Mode MEMS Gyroscope

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2022-09-13 DOI:10.1109/JSEN.2022.3204431
Xuetong Wang;Xudong Zheng;Yaojie Shen;Chenhao Xia;Guowen Liu;Zhonghe Jin;Zhipeng Ma
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引用次数: 7

Abstract

This article proposes a digital control structure for a doubly decoupled gyroscope working in the Lissajous frequency-modulated (LFM) mode based on digital phase-locked loops (PLLs), which demodulates the angular rate signal directly from the readable digital gyroscope resonance frequency, eliminating the need for specific frequency readout circuits. The resonance frequencies of the LFM gyroscope working modes contain a mode mismatch frequency modulated by input angular rate, respectively, which are tracked by two digital PLLs followed by subsequent digital demodulation and filtering. A linearized model of the digital PLL is built to analyze noise and control characteristics for different PI parameters. The impact of different amplitude–phase extraction architectures on the extracted phase signal is also addressed in this article. Contrast experiments are carried out using the same gyroscope with large internal thermal stress due to the silicon-glass bonding process and no stress relief structure around the sensing element, working in the traditional amplitude-modulated (AM) mode and the LFM mode. Overall, the LFM working mode maintains its low-temperature sensitivity and high stability in spite of the large internal thermal stress in the gyroscope compared to AM working modes. The maximum scale factor variation over the temperature range from 10 °C to 50 °C is 1000 ppm for the LFM mode compared to 51 900 ppm for the AM closed mode. The maximum zero rate output drift over the same temperature range is 0.1248 °/s for the LFM mode and 10.7139 °/s for the AM closed mode. The scale factor nonlinearity is 329 ppm with an angular rate input range of ±50 °/s for the LFM mode compared to 1902 ppm for the AM closed mode. The LFM mode zero-bias fluctuation for ten days is less than 0.12 °/s. The angle random walk (ARW) and the bias instability (BI) of the LFM gyroscope are 0.316 °/ $\surd \text{h}$ and 2 °/h, respectively.
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Lissajous调频模MEMS陀螺仪的数字控制结构
本文提出了一种基于数字锁相环(pll)的双解耦利萨约调频(LFM)模式陀螺仪的数字控制结构,该结构直接从可读数字陀螺仪的谐振频率中解调角速率信号,而无需特定的频率读出电路。LFM陀螺仪工作模式的谐振频率分别包含一个由输入角速率调制的模式失配频率,由两个数字锁相环跟踪,然后进行数字解调和滤波。建立了数字锁相环的线性化模型,分析了不同PI参数下的噪声和控制特性。本文还讨论了不同幅度相位提取架构对提取的相位信号的影响。采用同一陀螺仪进行对比实验,该陀螺仪由于采用硅-玻璃键合工艺,传感元件周围无应力消除结构,内部热应力较大,工作在传统的调幅模式和线性调频模式下。总体而言,尽管与AM工作模式相比陀螺仪内部热应力较大,但LFM工作模式保持了低温灵敏度和高稳定性。在10°C至50°C的温度范围内,LFM模式的最大比例因子变化为1000 ppm,而AM关闭模式的最大比例因子变化为51 900 ppm。在相同温度范围内,LFM模式的最大零速率输出漂移为0.1248°/s, AM关闭模式的最大零速率输出漂移为10.7139°/s。线性调频模式的比例系数非线性为329 ppm,角速率输入范围为±50°/s,而调幅关闭模式的比例系数非线性为1902 ppm。LFM模式10天零偏波动小于0.12°/s。LFM陀螺仪的角度随机游走(ARW)和偏置不稳定性(BI)分别为0.316°/ $\surd \text{h}$和2°/h。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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