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UBC Theses and Dissertations

Reinforcement-learning control framework and sensing paradigm for flapping-wing micro aerial vehicles Motamed, Mehran


Insects are fascinating for their maneuverability and complex aerobatics. Flapping-wing micro aerial vehicles are inspired from insect flight and aim to achieve high maneuverability at low speeds as well as hovering. Such a vehicle would have unique applications in social and economic sectors as well as in the military. This work, introduces a learning approach to flight control for flapping-wing micro aerial vehicles. A reinforcement-learning control framework has been proposed as a suitable biomimetic candidate for control of micro aerial vehicles. This work also discusses a matching sensing paradigm as a byproduct of the control approach. The control framework is then implemented using the Q-learning algorithm for the case study of lift generation for microflight. The results from a computer simulation using a quasi-steady aerodynamic model, and from an experimental investigation on a dynamically scaled model, confirm the applicability of the proposed framework. Moreover, the results of the learning scheme are shown to be comparable to a biological fruit fly, Drosophila melanogaster, in terms of the mean lift-force coefficient and the mean aerodynamic efficiency.

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