Outlier‐robust zonotope set‐membership filter for discrete‐time nonlinear system
Transient sensor malfunction, improper replacement measures, and large non‐Gaussian noise can cause data outliers in real‐world scenarios. In this article, an outlier‐robust zonotope set‐membership filter is proposed for uncertain nonlinear systems susceptible to outliers. In order to linearize nonl...
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| Published in | International journal of robust and nonlinear control Vol. 33; no. 14; pp. 8693 - 8714 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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25.09.2023
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| Abstract | Transient sensor malfunction, improper replacement measures, and large non‐Gaussian noise can cause data outliers in real‐world scenarios. In this article, an outlier‐robust zonotope set‐membership filter is proposed for uncertain nonlinear systems susceptible to outliers. In order to linearize nonlinear systems, a linearization method based on Stirling's interpolation formula is proposed. Then, an outlier detector utilizing the linearization error bounds, the prediction domain, and the output feasible domain is constructed. Based on the linearized system, a tight strip construction method is given to reduce the conservatism of the estimated enclosure. Moreover, a prediction‐correction‐backward reconstruction filter structure is designed to improve estimation accuracy and reduce conservatism. The proposed structure fuses historical data to attenuate the impact of uncertainty on the system. The robustness, guarantee, and convergence of the proposed filter are investigated. Finally, the effectiveness of the proposed approach is evaluated by two simulation examples. |
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| AbstractList | Transient sensor malfunction, improper replacement measures, and large non‐Gaussian noise can cause data outliers in real‐world scenarios. In this article, an outlier‐robust zonotope set‐membership filter is proposed for uncertain nonlinear systems susceptible to outliers. In order to linearize nonlinear systems, a linearization method based on Stirling's interpolation formula is proposed. Then, an outlier detector utilizing the linearization error bounds, the prediction domain, and the output feasible domain is constructed. Based on the linearized system, a tight strip construction method is given to reduce the conservatism of the estimated enclosure. Moreover, a prediction‐correction‐backward reconstruction filter structure is designed to improve estimation accuracy and reduce conservatism. The proposed structure fuses historical data to attenuate the impact of uncertainty on the system. The robustness, guarantee, and convergence of the proposed filter are investigated. Finally, the effectiveness of the proposed approach is evaluated by two simulation examples. |
| Author | Yang, Bo Cheng, Rong Huang, Jian Chen, Xinxing |
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| Cites_doi | 10.1109/TAC.2021.3081078 10.1049/iet‐cta.2019.0004 10.1016/j.automatica.2012.09.020 10.1016/j.automatica.2016.01.015 10.1002/rnc.5036 10.1002/rnc.3030 10.23919/ACC.2018.8431250 10.1002/rnc.3319 10.1134/s0005117916020016 10.1201/9780203026922 10.1109/TAC.2019.2902673 10.1007/s10514‐015‐9447‐y 10.1002/rnc.5948 10.1016/j.automatica.2007.05.008 10.1016/j.automatica.2004.02.014 10.1016/j.automatica.2004.12.008 10.1109/TAC.2018.2816262 10.1016/j.automatica.2013.07.013 10.1016/j.automatica.2017.10.022 10.1109/MRA.2006.1638022 10.1002/rnc.856 10.1002/rnc.2842 10.1016/j.automatica.2020.108867 10.1016/S0005‐1098(00)00089‐3 10.1007/s11432‐017‐9242‐8 10.1016/0005‐1098(96)00048‐9 10.1109/TIE.2019.2962426 10.1109/TAC.2006.878750 10.1007/bf02684450 10.1109/JAS.2021.1003826 10.1109/TASE.2016.2602319 10.1109/TSMC.2020.2970237 10.1109/TAC.2021.3081256 10.1109/TSP.2009.2039731 10.1109/TRO.2008.2010358 10.1016/j.automatica.2016.02.036 |
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| SubjectTerms | Discrete time systems discrete‐time nonlinear system Historical structures Interpolation Linearization Nonlinear systems outliers Outliers (statistics) Random noise Robustness set‐membership filter zonotope |
| Title | Outlier‐robust zonotope set‐membership filter for discrete‐time nonlinear system |
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