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Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable
This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fau...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9001130/ https://www.ncbi.nlm.nih.gov/pubmed/35419041 http://dx.doi.org/10.1155/2022/8339634 |
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author | Huang, Hanqiao Cheng, Haoyu Song, Ruijia Sun, Gonghao Fang, Yangwang Huang, Guan |
author_facet | Huang, Hanqiao Cheng, Haoyu Song, Ruijia Sun, Gonghao Fang, Yangwang Huang, Guan |
author_sort | Huang, Hanqiao |
collection | PubMed |
description | This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper. |
format | Online Article Text |
id | pubmed-9001130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90011302022-04-12 Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable Huang, Hanqiao Cheng, Haoyu Song, Ruijia Sun, Gonghao Fang, Yangwang Huang, Guan Comput Intell Neurosci Research Article This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper. Hindawi 2022-04-04 /pmc/articles/PMC9001130/ /pubmed/35419041 http://dx.doi.org/10.1155/2022/8339634 Text en Copyright © 2022 Hanqiao Huang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Huang, Hanqiao Cheng, Haoyu Song, Ruijia Sun, Gonghao Fang, Yangwang Huang, Guan Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title | Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title_full | Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title_fullStr | Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title_full_unstemmed | Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title_short | Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable |
title_sort | fault detection filter design and optimization for switched systems with all modes unstable |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9001130/ https://www.ncbi.nlm.nih.gov/pubmed/35419041 http://dx.doi.org/10.1155/2022/8339634 |
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