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Optimal Vertically Ascending Flight of an Insect-Like Flapping-Wing Micro Air Vehicle

5 pagesPublished: May 14, 2020

Abstract

This paper explores the optimal flight condition of an insect-like flapping-wing micro air vehicle (FWMAV) while ascending vertically at constant speeds. The FWMAV is assumed to have the same mass properties and wing geometry as those of the hawkmoth Manduca sexta. The optimization is conducted through the combination of an artificial neural network and a genetic algorithm. The training data for the artificial neural network are provided by the unsteady vortex-lattice method written in the programming language FORTRAN using parallel computation techniques. The results show that the FWMAV has to alter its wing kinematics and flapping frequency to sustain vertically ascending flight. Moreover, while ascending, the FWMAV requires more energy than that in hover. The findings from this work are useful for the design of control strategies used for insect-like FWMAVs

Keyphrases: artificial neural network, flapping wing micro air vehicle, genetic algorithm, insect like, vertically ascending flapping flight

In: Tich Thien Truong, Trung Nghia Tran, Quoc Khai Le and Thanh Nha Nguyen (editors). Proceedings of International Symposium on Applied Science 2019, vol 3, pages 163-167.

BibTeX entry
@inproceedings{ISAS2019:Optimal_Vertically_Ascending_Flight,
  author    = {Anh Tuan Nguyen and Hoang Quan Dinh and Van Thang Nguyen and Thanh Le Vu Dan and Thanh Dong Pham and Dinh Quang Nguyen},
  title     = {Optimal Vertically Ascending Flight of an Insect-Like Flapping-Wing Micro Air Vehicle},
  booktitle = {Proceedings of International Symposium on Applied Science 2019},
  editor    = {Tich Thien Truong and Trung Nghia Tran and Quoc Khai Le and Thanh Nha Nguyen},
  series    = {Kalpa Publications in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {/publications/paper/3jk9},
  doi       = {10.29007/wndn},
  pages     = {163-167},
  year      = {2020}}
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