Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation

In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural...
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Autor*in:	Liu, Wenhui [verfasserIn] Lim, Cheng-Chew Shi, Peng Xu, Shengyuan

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016

Schlagwörter:	Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems

Übergeordnetes Werk:	Enthalten in: International journal of control - London : Taylor & Francis, 1965, 90(2017), 11, Seite 2433-16
Übergeordnetes Werk:	volume:90 ; year:2017 ; number:11 ; pages:2433-16

Links:	Volltext Link aufrufen

DOI / URN:	10.1080/00207179.2016.1250162

Katalog-ID:	OLC1996748610

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allfields	10.1080/00207179.2016.1250162 doi PQ20171228 (DE-627)OLC1996748610 (DE-599)GBVOLC1996748610 (PRQ)c1910-72f36ceb98e8defc80bdcef0abb758da8eda6b5316337094b778e246f988e1e40 (KEY)0006630320170000090001102433observerbasedtrackingcontrolformimopurefeedbacknon DE-627 ger DE-627 rakwb eng 620 DNB Liu, Wenhui verfasserin aut Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems Lim, Cheng-Chew oth Shi, Peng oth Xu, Shengyuan oth Enthalten in International journal of control London : Taylor & Francis, 1965 90(2017), 11, Seite 2433-16 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:90 year:2017 number:11 pages:2433-16 http://dx.doi.org/10.1080/00207179.2016.1250162 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 90 2017 11 2433-16
spelling	10.1080/00207179.2016.1250162 doi PQ20171228 (DE-627)OLC1996748610 (DE-599)GBVOLC1996748610 (PRQ)c1910-72f36ceb98e8defc80bdcef0abb758da8eda6b5316337094b778e246f988e1e40 (KEY)0006630320170000090001102433observerbasedtrackingcontrolformimopurefeedbacknon DE-627 ger DE-627 rakwb eng 620 DNB Liu, Wenhui verfasserin aut Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems Lim, Cheng-Chew oth Shi, Peng oth Xu, Shengyuan oth Enthalten in International journal of control London : Taylor & Francis, 1965 90(2017), 11, Seite 2433-16 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:90 year:2017 number:11 pages:2433-16 http://dx.doi.org/10.1080/00207179.2016.1250162 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 90 2017 11 2433-16
allfields_unstemmed	10.1080/00207179.2016.1250162 doi PQ20171228 (DE-627)OLC1996748610 (DE-599)GBVOLC1996748610 (PRQ)c1910-72f36ceb98e8defc80bdcef0abb758da8eda6b5316337094b778e246f988e1e40 (KEY)0006630320170000090001102433observerbasedtrackingcontrolformimopurefeedbacknon DE-627 ger DE-627 rakwb eng 620 DNB Liu, Wenhui verfasserin aut Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems Lim, Cheng-Chew oth Shi, Peng oth Xu, Shengyuan oth Enthalten in International journal of control London : Taylor & Francis, 1965 90(2017), 11, Seite 2433-16 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:90 year:2017 number:11 pages:2433-16 http://dx.doi.org/10.1080/00207179.2016.1250162 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 90 2017 11 2433-16
allfieldsGer	10.1080/00207179.2016.1250162 doi PQ20171228 (DE-627)OLC1996748610 (DE-599)GBVOLC1996748610 (PRQ)c1910-72f36ceb98e8defc80bdcef0abb758da8eda6b5316337094b778e246f988e1e40 (KEY)0006630320170000090001102433observerbasedtrackingcontrolformimopurefeedbacknon DE-627 ger DE-627 rakwb eng 620 DNB Liu, Wenhui verfasserin aut Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems Lim, Cheng-Chew oth Shi, Peng oth Xu, Shengyuan oth Enthalten in International journal of control London : Taylor & Francis, 1965 90(2017), 11, Seite 2433-16 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:90 year:2017 number:11 pages:2433-16 http://dx.doi.org/10.1080/00207179.2016.1250162 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 90 2017 11 2433-16
allfieldsSound	10.1080/00207179.2016.1250162 doi PQ20171228 (DE-627)OLC1996748610 (DE-599)GBVOLC1996748610 (PRQ)c1910-72f36ceb98e8defc80bdcef0abb758da8eda6b5316337094b778e246f988e1e40 (KEY)0006630320170000090001102433observerbasedtrackingcontrolformimopurefeedbacknon DE-627 ger DE-627 rakwb eng 620 DNB Liu, Wenhui verfasserin aut Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Adaptive backstepping control neural networks input quantisation multiple-input and multiple-output (MIMO) pure-feedback nonlinear systems Lim, Cheng-Chew oth Shi, Peng oth Xu, Shengyuan oth Enthalten in International journal of control London : Taylor & Francis, 1965 90(2017), 11, Seite 2433-16 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:90 year:2017 number:11 pages:2433-16 http://dx.doi.org/10.1080/00207179.2016.1250162 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 90 2017 11 2433-16
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author	Liu, Wenhui
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title	Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation
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title_sort	observer-based tracking control for mimo pure-feedback nonlinear systems with time-delay and input quantisation
title_auth	Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation
abstract	In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.
abstractGer	In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.
abstract_unstemmed	In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700
container_issue	11
title_short	Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation
url	http://dx.doi.org/10.1080/00207179.2016.1250162 http://www.tandfonline.com/doi/abs/10.1080/00207179.2016.1250162
remote_bool	false
author2	Lim, Cheng-Chew Shi, Peng Xu, Shengyuan
author2Str	Lim, Cheng-Chew Shi, Peng Xu, Shengyuan
ppnlink	129595780
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth oth oth
doi_str	10.1080/00207179.2016.1250162
up_date	2024-07-04T01:17:15.077Z
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