Adaptive Multiple-Model Control of A Class of Nonlinear Systems

In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of ident...
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Autor*in:	Chao Yang [verfasserIn] Yingmin Jia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	adaptive control multiple-model design nonlinear systems asymptotic tracking

Übergeordnetes Werk:	In: Journal of Robotics, Networking and Artificial Life (JRNAL) - Atlantis Press, 2017, 2(2015), 2
Übergeordnetes Werk:	volume:2 ; year:2015 ; number:2

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2991/jrnal.2015.2.2.1

Katalog-ID:	DOAJ078399459

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allfields	10.2991/jrnal.2015.2.2.1 doi (DE-627)DOAJ078399459 (DE-599)DOAJ799f69f1572648dca7a27212744128a2 DE-627 ger DE-627 rakwb eng Chao Yang verfasserin aut Adaptive Multiple-Model Control of A Class of Nonlinear Systems 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results. adaptive control multiple-model design nonlinear systems asymptotic tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 2(2015), 2 (DE-627)1005552487 23526386 nnns volume:2 year:2015 number:2 https://doi.org/10.2991/jrnal.2015.2.2.1 kostenfrei https://doaj.org/article/799f69f1572648dca7a27212744128a2 kostenfrei https://www.atlantis-press.com/article/25829.pdf kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 2
spelling	10.2991/jrnal.2015.2.2.1 doi (DE-627)DOAJ078399459 (DE-599)DOAJ799f69f1572648dca7a27212744128a2 DE-627 ger DE-627 rakwb eng Chao Yang verfasserin aut Adaptive Multiple-Model Control of A Class of Nonlinear Systems 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results. adaptive control multiple-model design nonlinear systems asymptotic tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 2(2015), 2 (DE-627)1005552487 23526386 nnns volume:2 year:2015 number:2 https://doi.org/10.2991/jrnal.2015.2.2.1 kostenfrei https://doaj.org/article/799f69f1572648dca7a27212744128a2 kostenfrei https://www.atlantis-press.com/article/25829.pdf kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 2
allfields_unstemmed	10.2991/jrnal.2015.2.2.1 doi (DE-627)DOAJ078399459 (DE-599)DOAJ799f69f1572648dca7a27212744128a2 DE-627 ger DE-627 rakwb eng Chao Yang verfasserin aut Adaptive Multiple-Model Control of A Class of Nonlinear Systems 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results. adaptive control multiple-model design nonlinear systems asymptotic tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 2(2015), 2 (DE-627)1005552487 23526386 nnns volume:2 year:2015 number:2 https://doi.org/10.2991/jrnal.2015.2.2.1 kostenfrei https://doaj.org/article/799f69f1572648dca7a27212744128a2 kostenfrei https://www.atlantis-press.com/article/25829.pdf kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 2
allfieldsGer	10.2991/jrnal.2015.2.2.1 doi (DE-627)DOAJ078399459 (DE-599)DOAJ799f69f1572648dca7a27212744128a2 DE-627 ger DE-627 rakwb eng Chao Yang verfasserin aut Adaptive Multiple-Model Control of A Class of Nonlinear Systems 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results. adaptive control multiple-model design nonlinear systems asymptotic tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 2(2015), 2 (DE-627)1005552487 23526386 nnns volume:2 year:2015 number:2 https://doi.org/10.2991/jrnal.2015.2.2.1 kostenfrei https://doaj.org/article/799f69f1572648dca7a27212744128a2 kostenfrei https://www.atlantis-press.com/article/25829.pdf kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 2
allfieldsSound	10.2991/jrnal.2015.2.2.1 doi (DE-627)DOAJ078399459 (DE-599)DOAJ799f69f1572648dca7a27212744128a2 DE-627 ger DE-627 rakwb eng Chao Yang verfasserin aut Adaptive Multiple-Model Control of A Class of Nonlinear Systems 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results. adaptive control multiple-model design nonlinear systems asymptotic tracking Technology T Yingmin Jia verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 2(2015), 2 (DE-627)1005552487 23526386 nnns volume:2 year:2015 number:2 https://doi.org/10.2991/jrnal.2015.2.2.1 kostenfrei https://doaj.org/article/799f69f1572648dca7a27212744128a2 kostenfrei https://www.atlantis-press.com/article/25829.pdf kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2015 2
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author	Chao Yang
spellingShingle	Chao Yang misc adaptive control misc multiple-model design misc nonlinear systems misc asymptotic tracking misc Technology misc T Adaptive Multiple-Model Control of A Class of Nonlinear Systems
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topic_title	Adaptive Multiple-Model Control of A Class of Nonlinear Systems adaptive control multiple-model design nonlinear systems asymptotic tracking
topic	misc adaptive control misc multiple-model design misc nonlinear systems misc asymptotic tracking misc Technology misc T
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title	Adaptive Multiple-Model Control of A Class of Nonlinear Systems
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title_full	Adaptive Multiple-Model Control of A Class of Nonlinear Systems
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title_sort	adaptive multiple-model control of a class of nonlinear systems
title_auth	Adaptive Multiple-Model Control of A Class of Nonlinear Systems
abstract	In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results.
abstractGer	In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results.
abstract_unstemmed	In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results.
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