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
This brief designs an observer-based adaptive finite-time neural control for a class of constrained nonlinear systems with external disturbances, and actuator saturation. First, a neural network (NN) state observer is developed to estimate the unmeasurable states. Combining the improved Gaussian function and an auxiliary compensation system, the actuator saturation can be solved. The ”explosion of complexity” problem is tackled by the finite-time command filter, and the filtering-error compensation system is constructed to resolve the filtering error. Moreover, the barrier Lyapunov function is incorporated into the controller design to satisfy the state constraints. By integrating the NN technique and the virtual parameter learning to approximate the bound of the lumped disturbance, the number of learning parameters is decreased. It can be proved that all the states do not transgress the predefined bounds and the tracking errors converge to bounded regions in finite time. Eventually, we provide comparative results to show the feasibility of the obtained results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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): | oai:eprints.whiterose.ac.uk:208292 |