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Updated: May 15, 2026
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Published on: July 12, 2014
Approximate Optimal Control for Morphing Aircraft via Attention Meta-Learning and Continual Learning
Summary
This study introduces an adaptive controller for morphing aircraft (MA) with unknown dynamics. The method uses meta-learning and continual learning to adapt to new flight conditions, ensuring optimal control strategies.
Area of Science:
- Aerospace Engineering
- Control Systems
- Artificial Intelligence
Background:
- Morphing aircraft (MA) present complex control challenges due to unknown aerodynamic-deformation relationships.
- Existing control methods struggle with the adaptive nature of MA across diverse flight scenarios.
Purpose of the Study:
- To develop an innovative approximate optimal controller for variable-span MA with unknown dynamic models.
- To enable adaptive control for novel deformation conditions encountered during flight.
Main Methods:
- A meta-learning (MetaL) framework with adversarial optimization extracts common invariant features from offline data.
- A squeeze-and-excitation (SE) network enhances feature representation via channel recalibration.
- Continual learning and concurrent learning (ConcL) update features and linear coefficients for online adaptation.
- A model-based reinforcement learning (RL) framework addresses the optimal control problem.
Main Results:
- The proposed controller effectively extracts invariant features and adapts to new morphing configurations.
- Nonsmooth Lyapunov stability analysis confirms the convergence of control strategies to optimal solutions.
- Numerical simulations validate the methodology's effectiveness for MA control.
Conclusions:
- The developed controller offers a robust solution for optimal control of MA with unknown dynamics.
- The integration of meta-learning, continual learning, and RL provides a powerful framework for adaptive flight control.
- This approach enhances the adaptability and performance of morphing aircraft systems.
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