Observer-based adaptive finite-time neural control for constrained nonlinear systems with actuator saturation compensation

Liu, K., Yang, P. orcid.org/0000-0002-8553-7127, Jiao, L. et al. (3 more authors) (2024) Observer-based adaptive finite-time neural control for constrained nonlinear systems with actuator saturation compensation. IEEE Transactions on Instrumentation and Measurement, 73. 7502516. ISSN 0018-9456

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2024 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Instrumentation and Measurement is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Actuator saturation; full-state constraints; finite-time control; neural networks; state observer
Dates:
  • Published: 27 February 2024
  • Published (online): 27 February 2024
  • Accepted: 10 January 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
UNSPECIFIED
INNOVATE UK
10050919 TS/X014096/1
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL (STFC) FOOD NETWORK+
UNSPECIFIED
Depositing User: Symplectic Sheffield
Date Deposited: 29 Jan 2024 15:48
Last Modified: 08 Nov 2024 13:14
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TIM.2024.3370753
Open Archives Initiative ID (OAI ID):

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