Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

Wen, S; Chen, MZQ; Zeng, Z; Huang, T; Li, C

Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

Wen, S

Chen, MZQ Zeng, Z Huang, T Li, C

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man and Cybernetics: Systems, 2017, 47, (8), pp. 2268-2278
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Chen, MZQ
dc.contributor.author	Zeng, Z
dc.contributor.author	Huang, T
dc.contributor.author	Li, C
dc.date.accessioned	2022-08-30T01:09:41Z
dc.date.available	2022-08-30T01:09:41Z
dc.date.issued	2017-08-01
dc.identifier.citation	IEEE Transactions on Systems, Man and Cybernetics: Systems, 2017, 47, (8), pp. 2268-2278
dc.identifier.issn	1083-4427
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/161084
dc.description.abstract	This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	National Natural Science Foundation of China61673187
dc.relation	National Natural Science Foundation of China61403152
dc.relation.ispartof	IEEE Transactions on Systems, Man and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2017.2648826
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems
dc.type	Journal Article
utslib.citation.volume	47
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-30T01:09:40Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	47
utslib.citation.issue	8

Abstract:

This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results.

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http://hdl.handle.net/10453/161084