Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints
Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the correspondi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Sai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Multiple-input and multiple-output nonlinear system Tangent barrier Lyapunov function |
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| Anmerkung: |
© The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 25(2023), 8 vom: 27. Juli, Seite 3144-3161 |
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| Übergeordnetes Werk: |
volume:25 ; year:2023 ; number:8 ; day:27 ; month:07 ; pages:3144-3161 |
| Links: |
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| DOI / URN: |
10.1007/s40815-023-01560-8 |
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| Katalog-ID: |
SPR053927427 |
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| 520 | |a Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. | ||
| 650 | 4 | |a Multiple-input and multiple-output nonlinear system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Full-state constraint |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tangent barrier Lyapunov function |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Adaptive self-triggered control |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Command filtering method |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zong, Guangdeng |4 aut | |
| 700 | 1 | |a Wang, Huanqing |4 aut | |
| 700 | 1 | |a Zhao, Xudong |4 aut | |
| 700 | 1 | |a Alharbi, Khalid H. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of fuzzy systems |d Taibei : Association, 2006 |g 25(2023), 8 vom: 27. Juli, Seite 3144-3161 |w (DE-627)612134636 |w (DE-600)2523322-1 |x 2199-3211 |7 nnns |
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10.1007/s40815-023-01560-8 doi (DE-627)SPR053927427 (SPR)s40815-023-01560-8-e DE-627 ger DE-627 rakwb eng Huang, Sai verfasserin aut Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 Zong, Guangdeng aut Wang, Huanqing aut Zhao, Xudong aut Alharbi, Khalid H. aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2023), 8 vom: 27. Juli, Seite 3144-3161 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2023 number:8 day:27 month:07 pages:3144-3161 https://dx.doi.org/10.1007/s40815-023-01560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2023 8 27 07 3144-3161 |
| spelling |
10.1007/s40815-023-01560-8 doi (DE-627)SPR053927427 (SPR)s40815-023-01560-8-e DE-627 ger DE-627 rakwb eng Huang, Sai verfasserin aut Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 Zong, Guangdeng aut Wang, Huanqing aut Zhao, Xudong aut Alharbi, Khalid H. aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2023), 8 vom: 27. Juli, Seite 3144-3161 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2023 number:8 day:27 month:07 pages:3144-3161 https://dx.doi.org/10.1007/s40815-023-01560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2023 8 27 07 3144-3161 |
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10.1007/s40815-023-01560-8 doi (DE-627)SPR053927427 (SPR)s40815-023-01560-8-e DE-627 ger DE-627 rakwb eng Huang, Sai verfasserin aut Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 Zong, Guangdeng aut Wang, Huanqing aut Zhao, Xudong aut Alharbi, Khalid H. aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2023), 8 vom: 27. Juli, Seite 3144-3161 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2023 number:8 day:27 month:07 pages:3144-3161 https://dx.doi.org/10.1007/s40815-023-01560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2023 8 27 07 3144-3161 |
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10.1007/s40815-023-01560-8 doi (DE-627)SPR053927427 (SPR)s40815-023-01560-8-e DE-627 ger DE-627 rakwb eng Huang, Sai verfasserin aut Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 Zong, Guangdeng aut Wang, Huanqing aut Zhao, Xudong aut Alharbi, Khalid H. aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2023), 8 vom: 27. Juli, Seite 3144-3161 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2023 number:8 day:27 month:07 pages:3144-3161 https://dx.doi.org/10.1007/s40815-023-01560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2023 8 27 07 3144-3161 |
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10.1007/s40815-023-01560-8 doi (DE-627)SPR053927427 (SPR)s40815-023-01560-8-e DE-627 ger DE-627 rakwb eng Huang, Sai verfasserin aut Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 Zong, Guangdeng aut Wang, Huanqing aut Zhao, Xudong aut Alharbi, Khalid H. aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2023), 8 vom: 27. Juli, Seite 3144-3161 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2023 number:8 day:27 month:07 pages:3144-3161 https://dx.doi.org/10.1007/s40815-023-01560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2023 8 27 07 3144-3161 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. 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| author |
Huang, Sai |
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Huang, Sai misc Multiple-input and multiple-output nonlinear system misc Full-state constraint misc Tangent barrier Lyapunov function misc Adaptive self-triggered control misc Command filtering method Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints |
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Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints Multiple-input and multiple-output nonlinear system (dpeaa)DE-He213 Full-state constraint (dpeaa)DE-He213 Tangent barrier Lyapunov function (dpeaa)DE-He213 Adaptive self-triggered control (dpeaa)DE-He213 Command filtering method (dpeaa)DE-He213 |
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misc Multiple-input and multiple-output nonlinear system misc Full-state constraint misc Tangent barrier Lyapunov function misc Adaptive self-triggered control misc Command filtering method |
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misc Multiple-input and multiple-output nonlinear system misc Full-state constraint misc Tangent barrier Lyapunov function misc Adaptive self-triggered control misc Command filtering method |
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Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints |
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Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints |
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Huang, Sai Zong, Guangdeng Wang, Huanqing Zhao, Xudong Alharbi, Khalid H. |
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command filter-based adaptive fuzzy self-triggered control for mimo nonlinear systems with time-varying full-state constraints |
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Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints |
| abstract |
Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This paper focuses on the adaptive fuzzy self-triggered tracking controller design for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is categorized into three steps: (1) restricting the states to satisfy the corresponding constraints; (2) handling the explosion of complexity; and (3) achieving a better compromise between system performances and communication loads. First, tangent barrier Lyapunov functions are applied to constrain the outputs and system states within time-varying boundaries. Then, the explosion of complexity is addressed via the command filtering method. Furthermore, an adaptive self-triggered control mechanism is developed to reduce resource consumption for each subsystem. In addition to solving the problem of monitoring the triggering threshold continuously, the designed adaptive self-triggered mechanism allows the triggering intervals to be dynamically adjusted according to the tracking errors, which makes the proposed control protocol possible to coordinate the system performances and communication resources. By using the Lyapunov stability criterion, it is demonstrated that all signals of the closed-loop systems are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to confirm the effectiveness of the proposed control approach. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints |
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https://dx.doi.org/10.1007/s40815-023-01560-8 |
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Zong, Guangdeng Wang, Huanqing Zhao, Xudong Alharbi, Khalid H. |
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Zong, Guangdeng Wang, Huanqing Zhao, Xudong Alharbi, Khalid H. |
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10.1007/s40815-023-01560-8 |
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2024-07-03T22:57:48.480Z |
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| score |
7.398164 |