Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter
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 du...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2449156 |
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| author | Amar Rezoug Jamshed Iqbal Abdelkrim Nemra |
| author_facet | Amar Rezoug Jamshed Iqbal Abdelkrim Nemra |
| author_sort | Amar Rezoug |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-815635b53b8146c1b1c3e0c7a8e62237 |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-815635b53b8146c1b1c3e0c7a8e622372025-02-03T11:08:26ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2024.2449156Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopterAmar Rezoug0Jamshed Iqbal1Abdelkrim Nemra2Laboratoire des Technologies Innovantes, Ecole Nationale Supérieure des Technologies Avancées, Diplomatic City, Dergana-Bordj El Kiffan, Algiers, AlgeriaSchool of Computer Science, Faculty of Science and Engineering, University of Hull, Hull, UKLaboratoire Guidage et Navigation, Ecole Militaire Polytechniques, Bordj El Bahri, Algiers, AlgeriaThis 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.https://www.tandfonline.com/doi/10.1080/21642583.2024.2449156Metaheuristic optimizationfractional-order PID controllerfast grey wolf optimizerhelicopter control |
| spellingShingle | Amar Rezoug Jamshed Iqbal Abdelkrim Nemra Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter Systems Science & Control Engineering Metaheuristic optimization fractional-order PID controller fast grey wolf optimizer helicopter control |
| title | Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter |
| title_full | Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter |
| title_fullStr | Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter |
| title_full_unstemmed | Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter |
| title_short | Dual FOPID-neural network controller based on fast grey wolf optimizer: application to two-inputs two-outputs helicopter |
| title_sort | dual fopid neural network controller based on fast grey wolf optimizer application to two inputs two outputs helicopter |
| topic | Metaheuristic optimization fractional-order PID controller fast grey wolf optimizer helicopter control |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2449156 |
| work_keys_str_mv | AT amarrezoug dualfopidneuralnetworkcontrollerbasedonfastgreywolfoptimizerapplicationtotwoinputstwooutputshelicopter AT jamshediqbal dualfopidneuralnetworkcontrollerbasedonfastgreywolfoptimizerapplicationtotwoinputstwooutputshelicopter AT abdelkrimnemra dualfopidneuralnetworkcontrollerbasedonfastgreywolfoptimizerapplicationtotwoinputstwooutputshelicopter |