RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot

Ruenruedeepan, Nattapong; Bureerat, Sujin; Panagant, Natee; Pholdee, Nantiwat

doi:10.3390/aerospace12060461

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot

Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

^*

Author to whom correspondence should be addressed.

Aerospace 2025, 12(6), 461; https://doi.org/10.3390/aerospace12060461

Submission received: 11 February 2025 / Revised: 17 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025

(This article belongs to the Special Issue Advanced Aircraft Structural Design and Applications)

Download Versions Notes

Abstract

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.

Keywords: fixed-structure robust controller design; metaheuristics; autopilot design; many-objective optimization; surrogate model

Share and Cite

MDPI and ACS Style

Ruenruedeepan, N.; Bureerat, S.; Panagant, N.; Pholdee, N. RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot. Aerospace 2025, 12, 461. https://doi.org/10.3390/aerospace12060461

AMA Style

Ruenruedeepan N, Bureerat S, Panagant N, Pholdee N. RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot. Aerospace. 2025; 12(6):461. https://doi.org/10.3390/aerospace12060461

Chicago/Turabian Style

Ruenruedeepan, Nattapong, Sujin Bureerat, Natee Panagant, and Nantiwat Pholdee. 2025. "RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot" Aerospace 12, no. 6: 461. https://doi.org/10.3390/aerospace12060461

APA Style

Ruenruedeepan, N., Bureerat, S., Panagant, N., & Pholdee, N. (2025). RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot. Aerospace, 12(6), 461. https://doi.org/10.3390/aerospace12060461

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

RBF-Learning-Based Many-Objective Metaheuristic for Robust and Optimal Controller Design in Fixed-Structure Heading Autopilot

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI