A Discrete-time Integral Sliding Mode Predictive Control Using Iterative Dynamic Linearization Data Model
The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time a...
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| Vydané v: | International journal of control, automation, and systems Ročník 21; číslo 7; s. 2293 - 2302 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.07.2023
Springer Nature B.V 제어·로봇·시스템학회 |
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| ISSN: | 1598-6446, 2005-4092 |
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| Abstract | The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error. |
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| AbstractList | The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error. The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error. KCI Citation Count: 1 |
| Author | Han, Yaozhen Hou, Mingdong Pan, Weigang |
| Author_xml | – sequence: 1 givenname: Mingdong surname: Hou fullname: Hou, Mingdong organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University – sequence: 2 givenname: Weigang orcidid: 0000-0001-5252-1420 surname: Pan fullname: Pan, Weigang email: panweigang1980@163.com organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University – sequence: 3 givenname: Yaozhen surname: Han fullname: Han, Yaozhen organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University |
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| Cites_doi | 10.1109/TCST.2012.2196281 10.1109/TPEL.2017.2711098 10.1109/TCST.2017.2692730 10.1049/iet-cta.2014.0743 10.1177/0142331219900593 10.1360/aas-007-1061 10.1177/0142331216659130 10.1109/TIE.2019.2947838 10.1109/TNNLS.2017.2673865 10.1109/TIE.2018.2815942 10.1109/TNNLS.2017.2696040 10.1080/00207721.2020.1778114 10.1016/j.jfranklin.2019.01.003 10.1007/s00500-016-2349-x 10.1109/TIE.2016.2636126 10.1109/TNNLS.2016.2616885 10.1007/s00034-013-9729-6 10.1109/LCSYS.2018.2842186 10.1016/j.ins.2020.06.061 10.1002/asjc.2245 10.1007/s12555-017-0322-9 10.1038/s41598-021-01871-6 10.1109/TIE.2015.2504343 10.1002/acs.2737 10.1016/j.isatra.2019.02.033 10.1007/s12555-020-0866-y 10.1109/TNNLS.2019.2919441 10.1016/j.jfranklin.2020.03.019 10.1016/j.compchemeng.2015.03.003 10.1016/j.jprocont.2012.08.001 10.1007/s12555-019-0590-7 10.1109/TNNLS.2015.2461022 |
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| SubjectTerms | Control Control methods Control systems design Controllers Convergence Data models Design parameters Discrete time systems Engineering Iterative methods Linearization Mathematical analysis Mechatronics Nonlinear systems Parameter estimation Predictive control Regular Papers Robotics Sliding mode control Tracking errors 제어계측공학 |
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| Title | A Discrete-time Integral Sliding Mode Predictive Control Using Iterative Dynamic Linearization Data Model |
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