RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot
This paper presents an innovative many-objective metaheuristic (MnMH) algorithm designed to tackle the challenges of robust and optimal controller design for fixed-structure heading autopilots. The proposed approach leverages the radial basis function (RBF)-learning operator during the reproduction...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/461 |
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| author | Nattapong Ruenruedeepan Sujin Bureerat Natee Panagant Nantiwat Pholdee |
| author_facet | Nattapong Ruenruedeepan Sujin Bureerat Natee Panagant Nantiwat Pholdee |
| author_sort | Nattapong Ruenruedeepan |
| collection | DOAJ |
| description | This paper presents an innovative many-objective metaheuristic (MnMH) algorithm designed to tackle the challenges of robust and optimal controller design for fixed-structure heading autopilots. The proposed approach leverages the radial basis function (RBF)-learning operator during the reproduction phase of the MnMH to generate high-quality solutions. A key feature of the method is its generation of a target Pareto front, in which a z-surrogate model makes predictions to guide design solutions toward achieving optimal performance. The effectiveness of the new algorithm is validated through both fixed-structure heading autopilot controller design problems and standard benchmark optimization problems. Results consistently show that the proposed algorithm outperforms several existing MnMHs across all tested scenarios. This study offers valuable insights into many-objective optimization and demonstrates the algorithm’s potential for enhancing robust controller design in heading autopilot systems. |
| format | Article |
| id | doaj-art-6e7000c01df5429daccbb690cfdb6394 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-6e7000c01df5429daccbb690cfdb63942025-08-20T03:26:11ZengMDPI AGAerospace2226-43102025-05-0112646110.3390/aerospace12060461RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading AutopilotNattapong Ruenruedeepan0Sujin Bureerat1Natee Panagant2Nantiwat Pholdee3Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandSustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandSustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandSustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandThis paper presents an innovative many-objective metaheuristic (MnMH) algorithm designed to tackle the challenges of robust and optimal controller design for fixed-structure heading autopilots. The proposed approach leverages the radial basis function (RBF)-learning operator during the reproduction phase of the MnMH to generate high-quality solutions. A key feature of the method is its generation of a target Pareto front, in which a z-surrogate model makes predictions to guide design solutions toward achieving optimal performance. The effectiveness of the new algorithm is validated through both fixed-structure heading autopilot controller design problems and standard benchmark optimization problems. Results consistently show that the proposed algorithm outperforms several existing MnMHs across all tested scenarios. This study offers valuable insights into many-objective optimization and demonstrates the algorithm’s potential for enhancing robust controller design in heading autopilot systems.https://www.mdpi.com/2226-4310/12/6/461fixed-structure robust controller designmetaheuristicsautopilot designmany-objective optimizationsurrogate model |
| spellingShingle | Nattapong Ruenruedeepan Sujin Bureerat Natee Panagant Nantiwat Pholdee RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot Aerospace fixed-structure robust controller design metaheuristics autopilot design many-objective optimization surrogate model |
| title | RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot |
| title_full | RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot |
| title_fullStr | RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot |
| title_full_unstemmed | RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot |
| title_short | RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot |
| title_sort | rbf learning based many objective metaheuristic for robust and optimal controller design in fixed structure heading autopilot |
| topic | fixed-structure robust controller design metaheuristics autopilot design many-objective optimization surrogate model |
| url | https://www.mdpi.com/2226-4310/12/6/461 |
| work_keys_str_mv | AT nattapongruenruedeepan rbflearningbasedmanyobjectivemetaheuristicforrobustandoptimalcontrollerdesigninfixedstructureheadingautopilot AT sujinbureerat rbflearningbasedmanyobjectivemetaheuristicforrobustandoptimalcontrollerdesigninfixedstructureheadingautopilot AT nateepanagant rbflearningbasedmanyobjectivemetaheuristicforrobustandoptimalcontrollerdesigninfixedstructureheadingautopilot AT nantiwatpholdee rbflearningbasedmanyobjectivemetaheuristicforrobustandoptimalcontrollerdesigninfixedstructureheadingautopilot |