Observer‐Based Model‐Free Iterative Learning of Fault‐Tolerant Control for Subway Train Velocity Tracking Systems
This article proposes a model‐free adaptive iterative learning fault‐tolerant control (MFAilFTC) scheme for subway train velocity tracking, with actuator faults and over‐velocity constrains. First, the train dynamics is transformed into an iteration‐related dynamic linearization data model. Compared...
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| Veröffentlicht in: | International journal of robust and nonlinear control |
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| Sprache: | Englisch |
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15.08.2025
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| ISSN: | 1049-8923, 1099-1239 |
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| Abstract | This article proposes a model‐free adaptive iterative learning fault‐tolerant control (MFAilFTC) scheme for subway train velocity tracking, with actuator faults and over‐velocity constrains. First, the train dynamics is transformed into an iteration‐related dynamic linearization data model. Compared to previous results, the learning strategy we propose shows enhanced performance in trajectory tracking by utilizing the partial form dynamic linearization technique, which significantly accelerates the iteration process. Next, to deal with non‐repetitive uncertainties, we present an iterative output observer to estimate inaccurate outputs ruined by non‐repetitive uncertainties. With the introduction of iterative learning observer (ILO), the tracking accuracy and the ability to suppress non‐repetitive uncertainties are improved. Additionally, the inclusion of the tracking error integral term in the ILO enhances the convergence speed. An over‐velocity constrains mechanism is developed to ensure that the train operates within a safe velocity range. Through rigorous mathematical analysis, it is demonstrated that the MFAilFTC method with the over‐velocity constrains mechanism ensures the train velocity tracking error converges along the iteration axis, indicating safe and reliable operation. Simulation results further validate the effectiveness of the proposed method. |
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| AbstractList | This article proposes a model‐free adaptive iterative learning fault‐tolerant control (MFAilFTC) scheme for subway train velocity tracking, with actuator faults and over‐velocity constrains. First, the train dynamics is transformed into an iteration‐related dynamic linearization data model. Compared to previous results, the learning strategy we propose shows enhanced performance in trajectory tracking by utilizing the partial form dynamic linearization technique, which significantly accelerates the iteration process. Next, to deal with non‐repetitive uncertainties, we present an iterative output observer to estimate inaccurate outputs ruined by non‐repetitive uncertainties. With the introduction of iterative learning observer (ILO), the tracking accuracy and the ability to suppress non‐repetitive uncertainties are improved. Additionally, the inclusion of the tracking error integral term in the ILO enhances the convergence speed. An over‐velocity constrains mechanism is developed to ensure that the train operates within a safe velocity range. Through rigorous mathematical analysis, it is demonstrated that the MFAilFTC method with the over‐velocity constrains mechanism ensures the train velocity tracking error converges along the iteration axis, indicating safe and reliable operation. Simulation results further validate the effectiveness of the proposed method. |
| Author | Chi, Ronghu Wang, Rongrong |
| Author_xml | – sequence: 1 givenname: Rongrong surname: Wang fullname: Wang, Rongrong organization: College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China – sequence: 2 givenname: Ronghu orcidid: 0000-0002-1325-7863 surname: Chi fullname: Chi, Ronghu organization: College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China |
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| Cites_doi | 10.1109/TITS.2020.2970000 10.1002/asjc.2010 10.1109/TSMC.2019.2957299 10.1109/TIV.2023.3305497 10.1109/TIV.2024.3379942 10.1109/TFUZZ.2020.2999958 10.1109/TASE.2012.2216261 10.1016/j.jprocont.2012.05.016 10.1109/CDC.2001.980906 10.1115/1.2719773 10.1016/j.ast.2020.105706 10.1109/TCYB.2019.2905427 10.1109/TITS.2020.3046416 10.1109/TASE.2014.2371816 10.1109/TME.1964.4323124 10.1109/TITS.2021.3106653 10.1016/j.automatica.2008.07.011 10.1109/TNNLS.2020.3027651 10.1109/TVT.2022.3174864 10.1109/SYSTOL.2010.5676066 10.1002/acs.936 10.1002/rnc.6287 10.1360/aas-007-1061 10.1109/TASE.2020.3041952 10.1109/TVT.2021.3133858 10.1016/j.jprocont.2017.08.013 10.1002/rob.4620010203 10.1109/TCYB.2020.2986006 |
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