Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays

Sung Jin Yoo; Jin Bae Park

doi:10.1109/TSMCB.2009.2016110

Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays

Sung Jin Yoo, Jin Bae Park

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

188 Citations (Scopus)

Abstract

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.

Original language	English
Pages (from-to)	1316-1323
Number of pages	8
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	39
Issue number	5
DOIs	https://doi.org/10.1109/TSMCB.2009.2016110
Publication status	Published - 2009

Bibliographical note

Funding Information:
Manuscript received September 26, 2008; revised January 29, 2009. First published March 31, 2009; current version published September 16, 2009. This work was supported in part by Korean Government (MOEHRD, Basic Research Promotion Fund) under Korea Research Foundation Grant KRF-2007-313-D00262 and in part by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 Program. This paper was recommended by Associate Editor R. Selmic.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMCB.2009.2016110

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title = "Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays",

abstract = "A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.",

keywords = "Decentralized adaptive control, Dynamic surface control (DSC), Function approximation technique, Strict-feedback form, Unknowntime delay",

author = "Yoo, {Sung Jin} and Park, {Jin Bae}",

note = "Funding Information: Manuscript received September 26, 2008; revised January 29, 2009. First published March 31, 2009; current version published September 16, 2009. This work was supported in part by Korean Government (MOEHRD, Basic Research Promotion Fund) under Korea Research Foundation Grant KRF-2007-313-D00262 and in part by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 Program. This paper was recommended by Associate Editor R. Selmic.",

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doi = "10.1109/TSMCB.2009.2016110",

language = "English",

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AU - Yoo, Sung Jin

AU - Park, Jin Bae

N1 - Funding Information: Manuscript received September 26, 2008; revised January 29, 2009. First published March 31, 2009; current version published September 16, 2009. This work was supported in part by Korean Government (MOEHRD, Basic Research Promotion Fund) under Korea Research Foundation Grant KRF-2007-313-D00262 and in part by Yonsei University Institute of TMS Information Technology, a Brain Korea 21 Program. This paper was recommended by Associate Editor R. Selmic.

PY - 2009

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N2 - A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.

AB - A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.

KW - Decentralized adaptive control

KW - Dynamic surface control (DSC)

KW - Function approximation technique

KW - Strict-feedback form

KW - Unknowntime delay

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Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays

Abstract

Bibliographical note

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