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Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapu...

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Detalles Bibliográficos
Autores principales: Fei, Juntao, Fang, Yunmei, Yuan, Zhuli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693956/
https://www.ncbi.nlm.nih.gov/pubmed/33138090
http://dx.doi.org/10.3390/mi11110968
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author Fei, Juntao
Fang, Yunmei
Yuan, Zhuli
author_facet Fei, Juntao
Fang, Yunmei
Yuan, Zhuli
author_sort Fei, Juntao
collection PubMed
description This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.
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spelling pubmed-76939562020-11-28 Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller Fei, Juntao Fang, Yunmei Yuan, Zhuli Micromachines (Basel) Article This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation. MDPI 2020-10-29 /pmc/articles/PMC7693956/ /pubmed/33138090 http://dx.doi.org/10.3390/mi11110968 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fei, Juntao
Fang, Yunmei
Yuan, Zhuli
Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title_full Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title_fullStr Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title_full_unstemmed Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title_short Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller
title_sort adaptive fuzzy sliding mode control for a micro gyroscope with backstepping controller
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693956/
https://www.ncbi.nlm.nih.gov/pubmed/33138090
http://dx.doi.org/10.3390/mi11110968
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