Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults
In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced...
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| Veröffentlicht in: | International journal of robust and nonlinear control Jg. 33; H. 11; S. 6070 - 6086 |
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| Sprache: | Englisch |
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Bognor Regis
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25.07.2023
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| ISSN: | 1049-8923, 1099-1239 |
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| Abstract | In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously. |
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| AbstractList | In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously. Here, in this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously. |
| Author | Kang, Yunfeng Wang, Hong Yao, Lina Zhou, Jinglin |
| Author_xml | – sequence: 1 givenname: Yunfeng surname: Kang fullname: Kang, Yunfeng organization: Zhengzhou University – sequence: 2 givenname: Lina orcidid: 0000-0003-3819-5643 surname: Yao fullname: Yao, Lina email: michelle_lnxq@126.com organization: Zhengzhou University – sequence: 3 givenname: Jinglin surname: Zhou fullname: Zhou, Jinglin organization: Beijing University of Chemical Technology – sequence: 4 givenname: Hong orcidid: 0000-0002-9876-0176 surname: Wang fullname: Wang, Hong organization: Oak Ridge National Laboratory |
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| Cites_doi | 10.1109/TVT.2020.2977353 10.1109/TII.2019.2946882 10.1109/TASE.2019.2939052 10.1049/iet-cta.2018.6124 10.1109/TPEL.2020.3012964 10.1109/TASE.2011.2159265 10.1109/9.802925 10.1016/j.fss.2019.05.004 10.1109/JSYST.2020.2963909 10.1016/j.ifacol.2015.09.723 10.1016/j.jprocont.2014.12.009 10.1177/01423312211016929 10.1109/TAC.2019.2914257 10.1109/CASE49439.2021.9551625 10.1016/j.jfranklin.2013.11.009 10.1002/rnc.5057 10.1007/978-1-4471-0481-0 10.1109/9.508919 10.1016/j.automatica.2018.03.075 10.1109/FUZZY.2007.4295597 10.1109/TIE.2014.2361795 10.1504/IJMIC.2015.072620 10.1016/j.automatica.2018.09.022 10.1016/j.jfranklin.2018.08.027 10.1109/TCYB.2019.2951534 10.1016/j.automatica.2012.06.036 10.1109/TASE.2015.2392161 10.1049/iet-cta.2014.0096 10.1109/TSMC.2020.2970237 10.1109/TR.2018.2868949 10.1016/j.jfranklin.2013.02.021 10.1109/ACCESS.2020.2991138 10.1016/j.jfranklin.2005.04.007 |
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| Notes | Corrections added on 17 April 2023, after first online publication: the affiliation links of authors Jinglin Zhou and Hong Wang in the author byline have been corrected in this version. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 USDOE AC05-00OR22725 |
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| SubjectTerms | Algorithms Control algorithms Control systems Control theory ENGINEERING Fault detection Fault diagnosis fault estimation fault isolation Fault tolerance fault-tolerant control Faults model predictive control multiplicative faults Nonlinear control nonlinear stochastic distribution control systems Predictive control Probability density functions Probability theory Subsystems |
| Title | Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults |
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