Skip to main content

Research Repository

Advanced Search

Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter

Rezoug, Amar; Iqbal, Jamshed; Nemra, Abdelkrim

Authors

Amar Rezoug

Abdelkrim Nemra



Abstract

This research introduces a novel dual Fast Grey Wolf Optimizer (FGWO) combined with Radial Basis Function Neural Networks (RBFNN) for a Fractional-Order PID (FOPID) controller applied to a helicopter simulator. The proposed FGWO improves the standard Grey Wolf Optimizer (GWO) by enhancing hunting during the exploitation phase and increases robustness in convergence to the minimum value. FGWO optimizes the FOPID parameters using a novel objective function. The RBFNN is integrated to address the nonlinearities and uncertainties, while a dual block mitigates the coupling effects. The performance of the proposed controller is characterized by two simulation scenarios. The first scenario involved nine benchmark functions across thirty trials. Results demonstrated that the FGWO offered superior performance in terms of robustness and proximity to the global minimum compared to the GWO. The second scenario involved applying the controllers to the helicopter. Results evidenced that the dual-FOPID-FGWO (DRF-FG) controller achieved a 4.3363% faster response and 1.8199% higher precision than the GWO-based controller (DRF-G). The DRF-FG showed robustness in trajectory tracking compared to the controllers based on the Ant Lion Optimizer (DRF-A) and the Whale Optimization Algorithm (DRF-W). DRF-FG improved the average regulation performance by 1.702% and trajectory tracking by 0.152% compared with DRF-G.

Citation

Rezoug, A., Iqbal, J., & Nemra, A. (2025). Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter. Systems Science and Control Engineering, 13(1), Article 2449156. https://doi.org/10.1080/21642583.2024.2449156

Journal Article Type Article
Acceptance Date Dec 27, 2024
Online Publication Date Jan 12, 2025
Publication Date Jan 1, 2025
Deposit Date Jan 4, 2025
Publicly Available Date Jan 13, 2025
Journal Systems Science and Control Engineering
Print ISSN 2164-2583
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number 2449156
DOI https://doi.org/10.1080/21642583.2024.2449156
Keywords Metaheuristic optimization; Fractional-order PID controller; Fast grey wolf optimizer; Helicopter control
Public URL https://hull-repository.worktribe.com/output/5001353

Files

Published article (6.6 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.




You might also like



Downloadable Citations