Dual closed-loop control method for resonant integrated optic gyroscopes with combination differential modulation

Tao, Yu; Liu, WenYao; Nan, Chaoming; Wang, ShiXian; Ding, XinSheng; Liu, Lai; Bai, Yu; Zhou, YanRu; Xing, EnBo; Chen, Jianjun; Tang, Jun; Liu, Jun

doi:10.1016/j.optcom.2024.130264

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Furthermore, innovative structural designs, such as the design of an I-shaped bulk acoustic resonator, are leveraged to enhance temperature stability and enable dynamic frequency adjustments across a broad spectrum [8]. Tao et al explored a dual closed-loop control strategy featuring differential modulation for resonant integrated optic gyroscopes, enhancing detection precision and minimizing noise interference, which significantly improves gyroscope reliability and reduces control inaccuracies across varying conditions [9]. Reference [10] introduced a MEMS gyroscope with a unique design enhancing resilience to manufacturing and environmental changes.…”

Section: Introductionmentioning

confidence: 99%

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Zhang,

Zhu

et al. 2024

Micromachines

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Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

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Section: Introductionmentioning

confidence: 99%

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Zhang,

Zhu

et al. 2024

Micromachines

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Optimized Low‐Loss Ge₂Sb₂Te₅ Superlattice: Design, Fabrication and Application

Dong,

Wu,

Zhong

et al. 2024

Advanced Optical Materials

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Optical phase change materials (OPCMs) hold significant promise in photonic integrated circuits (PICs) due to their non‐volatile modulation capabilities through phase transition. Recently, integrating OPCMs with silicon devices has been leveraged to achieve multi‐level modulation in neural network computing. However, the conventional OPCM Ge2Sb2Te5 (GST), faces challenges in large‐scale PICs due to its large absorption. In this work, an optimized low‐loss GST superlattice is demonstrated, which significantly lowers crystalline attenuation coefficients from 2.69 to 0.539 dB µm−1, with negligible loss in amorphous state, while retaining excellent phase modulation capability. Additionally, 15 distinguishable extinction ratio states are achieved by manipulating irradiation energy for a hybrid Si microring resonator (MRR) embedded with GST superlattice. To further explore the potential application of GST superlattice in optical computing, an all‐optical tunable temporal differentiator is empirically demonstrated with a single hybrid MRR, achieving a broad dynamic differentiation order ranging from 0.7 to 1.3. The non‐volatile nature of OPCM facilitates low energy consumption and effectively optimizes chip density. The proposed structural design strategy to reduce the absorption loss is universal for any promising OPCMs feasible for large‐scale artificial intelligence‐driven PICs, including optical analog computing, optical switching and optical neural networks.

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Advancements in Piezoelectric‐Enabled Devices for Optical Communication

Roszkiewicz,

Garlińska,

Pregowska

2024

Physica Status Solidi (a)

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The ability of piezoelectric materials to convert mechanical energy into electric energy and vice versa has made them desirable in the wide range of applications that oscillate from medicine to the energetics industry. Their implementation in optical communication is often connected with the modulation or other manipulations of the light signals. In this article, the recent advancements in the field of piezoelectrics‐based devices and their promising benefits in optical communication are explored. The application of piezoelectrics‐based devices in optical communication allows dynamic control, modulation, and manipulation of optical signals that lead to a more reliable transmission. It turns out that a combination of artificial‐intelligence‐based algorithms with piezoelectrics can enhance the performance of these devices, including optimization of piezoelectric modulation, adaptive signal processing, control of optical components, and increase the level of energy efficiency. It can enhance signal quality, mitigate interference, and reduce noise‐connected issues. Moreover, this technological fusion can increase the security of optical communication systems. Finally, the potential future research lines are determined.

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Dual closed-loop control method for resonant integrated optic gyroscopes with combination differential modulation

Cited by 4 publications

References 31 publications

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Optimized Low‐Loss Ge₂Sb₂Te₅ Superlattice: Design, Fabrication and Application

Advancements in Piezoelectric‐Enabled Devices for Optical Communication

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Dual closed-loop control method for resonant integrated optic gyroscopes with combination differential modulation

Cited by 4 publications

References 31 publications

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

Optimized Low‐Loss Ge2Sb2Te5 Superlattice: Design, Fabrication and Application

Advancements in Piezoelectric‐Enabled Devices for Optical Communication

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Product

Resources

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Optimized Low‐Loss Ge₂Sb₂Te₅ Superlattice: Design, Fabrication and Application