An improved H∞ fuzzy filter for nonlinear sampled-data systems
Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Ho Jun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Control, Automation and Systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 15(2017), 3 vom: 27. März, Seite 1394-1404 |
|---|---|
| Übergeordnetes Werk: |
volume:15 ; year:2017 ; number:3 ; day:27 ; month:03 ; pages:1394-1404 |
| Links: |
|---|
| DOI / URN: |
10.1007/s12555-016-0088-5 |
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SPR026434172 |
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| 520 | |a Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. | ||
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10.1007/s12555-016-0088-5 doi (DE-627)SPR026434172 (SPR)s12555-016-0088-5-e DE-627 ger DE-627 rakwb eng Kim, Ho Jun verfasserin aut An improved H∞ fuzzy filter for nonlinear sampled-data systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. fuzzy filter (dpeaa)DE-He213 linear matrix inequality (LMI) (dpeaa)DE-He213 sampled-data system (dpeaa)DE-He213 Takagi–Sugeno (T–S) fuzzy model (dpeaa)DE-He213 Park, Jin Bae aut Joo, Young Hoon aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1394-1404 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1394-1404 https://dx.doi.org/10.1007/s12555-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1394-1404 |
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10.1007/s12555-016-0088-5 doi (DE-627)SPR026434172 (SPR)s12555-016-0088-5-e DE-627 ger DE-627 rakwb eng Kim, Ho Jun verfasserin aut An improved H∞ fuzzy filter for nonlinear sampled-data systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. fuzzy filter (dpeaa)DE-He213 linear matrix inequality (LMI) (dpeaa)DE-He213 sampled-data system (dpeaa)DE-He213 Takagi–Sugeno (T–S) fuzzy model (dpeaa)DE-He213 Park, Jin Bae aut Joo, Young Hoon aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1394-1404 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1394-1404 https://dx.doi.org/10.1007/s12555-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1394-1404 |
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10.1007/s12555-016-0088-5 doi (DE-627)SPR026434172 (SPR)s12555-016-0088-5-e DE-627 ger DE-627 rakwb eng Kim, Ho Jun verfasserin aut An improved H∞ fuzzy filter for nonlinear sampled-data systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. fuzzy filter (dpeaa)DE-He213 linear matrix inequality (LMI) (dpeaa)DE-He213 sampled-data system (dpeaa)DE-He213 Takagi–Sugeno (T–S) fuzzy model (dpeaa)DE-He213 Park, Jin Bae aut Joo, Young Hoon aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1394-1404 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1394-1404 https://dx.doi.org/10.1007/s12555-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1394-1404 |
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10.1007/s12555-016-0088-5 doi (DE-627)SPR026434172 (SPR)s12555-016-0088-5-e DE-627 ger DE-627 rakwb eng Kim, Ho Jun verfasserin aut An improved H∞ fuzzy filter for nonlinear sampled-data systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. fuzzy filter (dpeaa)DE-He213 linear matrix inequality (LMI) (dpeaa)DE-He213 sampled-data system (dpeaa)DE-He213 Takagi–Sugeno (T–S) fuzzy model (dpeaa)DE-He213 Park, Jin Bae aut Joo, Young Hoon aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1394-1404 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1394-1404 https://dx.doi.org/10.1007/s12555-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1394-1404 |
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10.1007/s12555-016-0088-5 doi (DE-627)SPR026434172 (SPR)s12555-016-0088-5-e DE-627 ger DE-627 rakwb eng Kim, Ho Jun verfasserin aut An improved H∞ fuzzy filter for nonlinear sampled-data systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. fuzzy filter (dpeaa)DE-He213 linear matrix inequality (LMI) (dpeaa)DE-He213 sampled-data system (dpeaa)DE-He213 Takagi–Sugeno (T–S) fuzzy model (dpeaa)DE-He213 Park, Jin Bae aut Joo, Young Hoon aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1394-1404 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1394-1404 https://dx.doi.org/10.1007/s12555-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1394-1404 |
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Kim, Ho Jun Park, Jin Bae Joo, Young Hoon |
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Elektronische Aufsätze |
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Kim, Ho Jun |
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10.1007/s12555-016-0088-5 |
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improved h∞ fuzzy filter for nonlinear sampled-data systems |
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An improved H∞ fuzzy filter for nonlinear sampled-data systems |
| abstract |
Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
| abstractGer |
Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
| abstract_unstemmed |
Abstract This paper presents an H∞ fuzzy filter design method for nonlinear sampled-data systems that are represented by a Takagi–Sugeno (T–S) fuzzy model. An error system between the T–S fuzzy system and the fuzzy filter is obtained to analyze the sampled-data system and solve the filter synthesis problem. To enhance the feasibility of the sampled-data fuzzy filter, an improved approach is proposed that not only captures the characteristics of the sampled-data system, but also eliminates a complex discretized model of the sampled-data system using a Lyapunov function and two null terms. In the sense of Lyapunov, relaxed sufficient conditions, which are derived in terms of linear matrix inequalities (LMIs), are obtained for both asymptotic stability and H∞ disturbance attenuation performance of the error system with less conservatism. Finally, simulation examples are presented to show the effectiveness of the proposed method. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
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An improved H∞ fuzzy filter for nonlinear sampled-data systems |
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