Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

This paper proposes a fractional-order nonsingular terminal sliding mode control of a MEMS gyroscope using a double loop recurrent neural network approximator. For the system stability, a nonsingular terminal sliding mode controller is formulated to guarantee the convergence. For higher accuracy and...

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Autores principales:	Juntao Fei, Zhe Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	MEMS gyroscope nonsingular terminal sliding mode control fractional-order calculus recurrent neural network uncertainty approximation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2b27a4e2ee0e453ebb5b6972e092140b
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spelling	oai:doaj.org-article:2b27a4e2ee0e453ebb5b6972e092140b2021-11-19T00:05:25ZMulti-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope2169-353610.1109/ACCESS.2020.3022675https://doaj.org/article/2b27a4e2ee0e453ebb5b6972e092140b2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9187820/https://doaj.org/toc/2169-3536This paper proposes a fractional-order nonsingular terminal sliding mode control of a MEMS gyroscope using a double loop recurrent neural network approximator. For the system stability, a nonsingular terminal sliding mode controller is formulated to guarantee the convergence. For higher accuracy and faster convergence, the fractional-order (FO) calculus is employed with additional degree of freedom. For the system robustness, the neural network is designed to approximate the lumped uncertainty. The inner recurrent loop and external recurrent loop are employed to provide a feedback signal to obtain satisfactory approximation accuracy. For higher adaptability of the neural network, the dynamic function is formulated and the updating law of the parameter is given. Furthermore, the Lyapunov stability theorem is employed to verify the asymptotical stability and convergence of system. Simulations for a MEMS gyroscope are studied to exhibit the superiority of the proposed control strategy.Juntao FeiZhe WangIEEEarticleMEMS gyroscopenonsingular terminal sliding mode controlfractional-order calculusrecurrent neural networkuncertainty approximationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 167965-167974 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	MEMS gyroscope nonsingular terminal sliding mode control fractional-order calculus recurrent neural network uncertainty approximation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	MEMS gyroscope nonsingular terminal sliding mode control fractional-order calculus recurrent neural network uncertainty approximation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Juntao Fei Zhe Wang Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
description	This paper proposes a fractional-order nonsingular terminal sliding mode control of a MEMS gyroscope using a double loop recurrent neural network approximator. For the system stability, a nonsingular terminal sliding mode controller is formulated to guarantee the convergence. For higher accuracy and faster convergence, the fractional-order (FO) calculus is employed with additional degree of freedom. For the system robustness, the neural network is designed to approximate the lumped uncertainty. The inner recurrent loop and external recurrent loop are employed to provide a feedback signal to obtain satisfactory approximation accuracy. For higher adaptability of the neural network, the dynamic function is formulated and the updating law of the parameter is given. Furthermore, the Lyapunov stability theorem is employed to verify the asymptotical stability and convergence of system. Simulations for a MEMS gyroscope are studied to exhibit the superiority of the proposed control strategy.
format	article
author	Juntao Fei Zhe Wang
author_facet	Juntao Fei Zhe Wang
author_sort	Juntao Fei
title	Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
title_short	Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
title_full	Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
title_fullStr	Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
title_full_unstemmed	Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope
title_sort	multi-loop recurrent neural network fractional-order terminal sliding mode control of mems gyroscope
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/2b27a4e2ee0e453ebb5b6972e092140b
work_keys_str_mv	AT juntaofei multilooprecurrentneuralnetworkfractionalorderterminalslidingmodecontrolofmemsgyroscope AT zhewang multilooprecurrentneuralnetworkfractionalorderterminalslidingmodecontrolofmemsgyroscope
_version_	1718420659334283264

Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

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