Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise

In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of th...
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Autor*in:	Wei Cao [verfasserIn] Jinjie Qiao [verfasserIn] Ming Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 66197-66205
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:66197-66205

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2918167

Katalog-ID:	DOAJ053246993

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520			\|a In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm.
650		4	\|a Singular systems
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allfields	10.1109/ACCESS.2019.2918167 doi (DE-627)DOAJ053246993 (DE-599)DOAJ46bac990079a45a0b3f29d8eb96a44d1 DE-627 ger DE-627 rakwb eng TK1-9971 Wei Cao verfasserin aut Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm. Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering Jinjie Qiao verfasserin aut Ming Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 66197-66205 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:66197-66205 https://doi.org/10.1109/ACCESS.2019.2918167 kostenfrei https://doaj.org/article/46bac990079a45a0b3f29d8eb96a44d1 kostenfrei https://ieeexplore.ieee.org/document/8719901/ kostenfrei https://doaj.org/toc/2169-3536 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 66197-66205
spelling	10.1109/ACCESS.2019.2918167 doi (DE-627)DOAJ053246993 (DE-599)DOAJ46bac990079a45a0b3f29d8eb96a44d1 DE-627 ger DE-627 rakwb eng TK1-9971 Wei Cao verfasserin aut Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm. Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering Jinjie Qiao verfasserin aut Ming Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 66197-66205 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:66197-66205 https://doi.org/10.1109/ACCESS.2019.2918167 kostenfrei https://doaj.org/article/46bac990079a45a0b3f29d8eb96a44d1 kostenfrei https://ieeexplore.ieee.org/document/8719901/ kostenfrei https://doaj.org/toc/2169-3536 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 66197-66205
allfields_unstemmed	10.1109/ACCESS.2019.2918167 doi (DE-627)DOAJ053246993 (DE-599)DOAJ46bac990079a45a0b3f29d8eb96a44d1 DE-627 ger DE-627 rakwb eng TK1-9971 Wei Cao verfasserin aut Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm. Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering Jinjie Qiao verfasserin aut Ming Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 66197-66205 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:66197-66205 https://doi.org/10.1109/ACCESS.2019.2918167 kostenfrei https://doaj.org/article/46bac990079a45a0b3f29d8eb96a44d1 kostenfrei https://ieeexplore.ieee.org/document/8719901/ kostenfrei https://doaj.org/toc/2169-3536 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 66197-66205
allfieldsGer	10.1109/ACCESS.2019.2918167 doi (DE-627)DOAJ053246993 (DE-599)DOAJ46bac990079a45a0b3f29d8eb96a44d1 DE-627 ger DE-627 rakwb eng TK1-9971 Wei Cao verfasserin aut Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm. Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering Jinjie Qiao verfasserin aut Ming Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 66197-66205 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:66197-66205 https://doi.org/10.1109/ACCESS.2019.2918167 kostenfrei https://doaj.org/article/46bac990079a45a0b3f29d8eb96a44d1 kostenfrei https://ieeexplore.ieee.org/document/8719901/ kostenfrei https://doaj.org/toc/2169-3536 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 66197-66205
allfieldsSound	10.1109/ACCESS.2019.2918167 doi (DE-627)DOAJ053246993 (DE-599)DOAJ46bac990079a45a0b3f29d8eb96a44d1 DE-627 ger DE-627 rakwb eng TK1-9971 Wei Cao verfasserin aut Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm. Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering Jinjie Qiao verfasserin aut Ming Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 66197-66205 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:66197-66205 https://doi.org/10.1109/ACCESS.2019.2918167 kostenfrei https://doaj.org/article/46bac990079a45a0b3f29d8eb96a44d1 kostenfrei https://ieeexplore.ieee.org/document/8719901/ kostenfrei https://doaj.org/toc/2169-3536 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 66197-66205
language	English
source	In IEEE Access 7(2019), Seite 66197-66205 volume:7 year:2019 pages:66197-66205
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topic_facet	Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Wei Cao @@aut@@ Jinjie Qiao @@aut@@ Ming Sun @@aut@@
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callnumber-first	T - Technology
author	Wei Cao
spellingShingle	Wei Cao misc TK1-9971 misc Singular systems misc iterative learning control misc gain self-regulation misc measurement noise misc tracking accuracy misc Electrical engineering. Electronics. Nuclear engineering Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise
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topic_title	TK1-9971 Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise Singular systems iterative learning control gain self-regulation measurement noise tracking accuracy
topic	misc TK1-9971 misc Singular systems misc iterative learning control misc gain self-regulation misc measurement noise misc tracking accuracy misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Singular systems misc iterative learning control misc gain self-regulation misc measurement noise misc tracking accuracy misc Electrical engineering. Electronics. Nuclear engineering
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title	Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise
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title_sort	learning gain self-regulation iterative learning control for suppressing singular system measurement noise
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title_auth	Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise
abstract	In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm.
abstractGer	In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm.
abstract_unstemmed	In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm.
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title_short	Learning Gain Self-Regulation Iterative Learning Control for Suppressing Singular System Measurement Noise
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up_date	2024-07-03T16:38:06.535Z
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