Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer

Rui Zhang; Bin Xu; Peng Shi

doi:10.1109/TNNLS.2020.3030712

Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer

Rui Zhang, Bin Xu, Peng Shi

School of Automation

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

Original language	English
Pages (from-to)	962-972
Number of pages	11
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	33
Issue number	3
DOIs	https://doi.org/10.1109/TNNLS.2020.3030712
State	Published - 1 Mar 2022

Keywords

Disturbance observer (DOB)
high gain observer (HGO)
micromechanical (MEMS) gyroscopes
neural networks (NNs)
output feedback control

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10.1109/TNNLS.2020.3030712

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title = "Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer",

abstract = "This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.",

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N2 - This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

AB - This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

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Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer

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