Attention-Based Multi-Objective Control for Morphing Aircraft
This paper proposes a learning-based joint morphing and flight control framework for avian-inspired morphing aircraft. Firstly, a novel multi-objective multi-phase optimal control problem is formulated to synthesize the comprehensive flight missions, incorporating additional requirements such as fue...
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MDPI AG
2025-04-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/5/280 |
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| author | Qien Fu Changyin Sun |
| author_facet | Qien Fu Changyin Sun |
| author_sort | Qien Fu |
| collection | DOAJ |
| description | This paper proposes a learning-based joint morphing and flight control framework for avian-inspired morphing aircraft. Firstly, a novel multi-objective multi-phase optimal control problem is formulated to synthesize the comprehensive flight missions, incorporating additional requirements such as fuel consumption, maneuverability, and agility of the morphing aircraft. Subsequently, an auxiliary problem, employing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ϵ</mi></semantics></math></inline-formula>-constraint and augmented state methods, is introduced to yield a finite and locally Lipschitz continuous value function, which facilitates the construction of a neural network controller. Furthermore, a multi-phase pseudospectral method is derived to discretize the auxiliary problem and formulate the corresponding nonlinear programming problem, where open loop optimal solutions of the multi-task flight mission are generated. Finally, a learning-based feedback controller is established using data from the open loop solutions, where a temporal masked attention mechanism is developed to extract information from sequential data more efficiently. Simulation results demonstrate that the designed attention module in the learning scheme yields a significant 53.5% reduction in test loss compared to the baseline model. Additionally, the proposed learning-based joint morphing and flight controller achieves a 37.6% improvement in average tracking performance over the fixed wing configuration, while also satisfying performance requirements for fuel consumption, maneuverability, and agility. |
| format | Article |
| id | doaj-art-9f1dd93e54774db69833244af755d413 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-9f1dd93e54774db69833244af755d4132025-08-20T01:56:25ZengMDPI AGBiomimetics2313-76732025-04-0110528010.3390/biomimetics10050280Attention-Based Multi-Objective Control for Morphing AircraftQien Fu0Changyin Sun1School of Automation, Southeast University, Nanjing 210096, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaThis paper proposes a learning-based joint morphing and flight control framework for avian-inspired morphing aircraft. Firstly, a novel multi-objective multi-phase optimal control problem is formulated to synthesize the comprehensive flight missions, incorporating additional requirements such as fuel consumption, maneuverability, and agility of the morphing aircraft. Subsequently, an auxiliary problem, employing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ϵ</mi></semantics></math></inline-formula>-constraint and augmented state methods, is introduced to yield a finite and locally Lipschitz continuous value function, which facilitates the construction of a neural network controller. Furthermore, a multi-phase pseudospectral method is derived to discretize the auxiliary problem and formulate the corresponding nonlinear programming problem, where open loop optimal solutions of the multi-task flight mission are generated. Finally, a learning-based feedback controller is established using data from the open loop solutions, where a temporal masked attention mechanism is developed to extract information from sequential data more efficiently. Simulation results demonstrate that the designed attention module in the learning scheme yields a significant 53.5% reduction in test loss compared to the baseline model. Additionally, the proposed learning-based joint morphing and flight controller achieves a 37.6% improvement in average tracking performance over the fixed wing configuration, while also satisfying performance requirements for fuel consumption, maneuverability, and agility.https://www.mdpi.com/2313-7673/10/5/280morphing aircraftmulti-objective controloptimal controlpseudospectral methodsattention mechanism |
| spellingShingle | Qien Fu Changyin Sun Attention-Based Multi-Objective Control for Morphing Aircraft Biomimetics morphing aircraft multi-objective control optimal control pseudospectral methods attention mechanism |
| title | Attention-Based Multi-Objective Control for Morphing Aircraft |
| title_full | Attention-Based Multi-Objective Control for Morphing Aircraft |
| title_fullStr | Attention-Based Multi-Objective Control for Morphing Aircraft |
| title_full_unstemmed | Attention-Based Multi-Objective Control for Morphing Aircraft |
| title_short | Attention-Based Multi-Objective Control for Morphing Aircraft |
| title_sort | attention based multi objective control for morphing aircraft |
| topic | morphing aircraft multi-objective control optimal control pseudospectral methods attention mechanism |
| url | https://www.mdpi.com/2313-7673/10/5/280 |
| work_keys_str_mv | AT qienfu attentionbasedmultiobjectivecontrolformorphingaircraft AT changyinsun attentionbasedmultiobjectivecontrolformorphingaircraft |