A combined feedforward-feedback controller design for nonlinear systems

This study presents a control framework that integrates both feedforward (FF) and feedback (FB) procedures to control nonlinear systems. The intelligent control component consisted of a modified recurrent wavelet neural network used to build the FF controller. Furthermore, the H-infinity control, re...

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Main Authors: Abdulkareem Jenan J., Ali Hazem I., Lutfy Omar Farouq
Format: Article
Language:English
Published: De Gruyter 2024-11-01
Series:Open Engineering
Subjects:
Online Access:https://doi.org/10.1515/eng-2024-0091
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author Abdulkareem Jenan J.
Ali Hazem I.
Lutfy Omar Farouq
author_facet Abdulkareem Jenan J.
Ali Hazem I.
Lutfy Omar Farouq
author_sort Abdulkareem Jenan J.
collection DOAJ
description This study presents a control framework that integrates both feedforward (FF) and feedback (FB) procedures to control nonlinear systems. The intelligent control component consisted of a modified recurrent wavelet neural network used to build the FF controller. Furthermore, the H-infinity control, renowned for its robust and durable attributes, was employed in the FB loop. The parameters of the controller were improved using particle swarm optimization. The proposed controller’s efficacy was compared with that of another FF-FB controller, in which the FF controller was a multilayer perceptron, and the FB controller was an H-infinity controller. The effectiveness of the proposed framework to control the nonlinear mass–spring dynamics was evaluated by analyzing its control accuracy and its capacity to tolerate external disturbances.
format Article
id doaj-art-95c55a2d4e9b4cd581c8f12b3c82ca36
institution OA Journals
issn 2391-5439
language English
publishDate 2024-11-01
publisher De Gruyter
record_format Article
series Open Engineering
spelling doaj-art-95c55a2d4e9b4cd581c8f12b3c82ca362025-08-20T02:23:35ZengDe GruyterOpen Engineering2391-54392024-11-011419384610.1515/eng-2024-0091A combined feedforward-feedback controller design for nonlinear systemsAbdulkareem Jenan J.0Ali Hazem I.1Lutfy Omar Farouq2Control and Systems Engineering Department, University of Technology, Baghdad, IraqControl and Systems Engineering Department, University of Technology, Baghdad, IraqControl and Systems Engineering Department, University of Technology, Baghdad, IraqThis study presents a control framework that integrates both feedforward (FF) and feedback (FB) procedures to control nonlinear systems. The intelligent control component consisted of a modified recurrent wavelet neural network used to build the FF controller. Furthermore, the H-infinity control, renowned for its robust and durable attributes, was employed in the FB loop. The parameters of the controller were improved using particle swarm optimization. The proposed controller’s efficacy was compared with that of another FF-FB controller, in which the FF controller was a multilayer perceptron, and the FB controller was an H-infinity controller. The effectiveness of the proposed framework to control the nonlinear mass–spring dynamics was evaluated by analyzing its control accuracy and its capacity to tolerate external disturbances.https://doi.org/10.1515/eng-2024-0091psorobust controllerintelligent controllerwavelet neural networkh-infinity
spellingShingle Abdulkareem Jenan J.
Ali Hazem I.
Lutfy Omar Farouq
A combined feedforward-feedback controller design for nonlinear systems
Open Engineering
pso
robust controller
intelligent controller
wavelet neural network
h-infinity
title A combined feedforward-feedback controller design for nonlinear systems
title_full A combined feedforward-feedback controller design for nonlinear systems
title_fullStr A combined feedforward-feedback controller design for nonlinear systems
title_full_unstemmed A combined feedforward-feedback controller design for nonlinear systems
title_short A combined feedforward-feedback controller design for nonlinear systems
title_sort combined feedforward feedback controller design for nonlinear systems
topic pso
robust controller
intelligent controller
wavelet neural network
h-infinity
url https://doi.org/10.1515/eng-2024-0091
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