Real-Time Learning of Efficient Lift Generation on a Dynamically Scaled Flapping Wing Using Policy Search

Yagiz E. Bayiz, Long Chen, Shih Jung Hsu, Pan Liu, Aaron N. Aguiles, Bo Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this work, we present a successful application of a policy search algorithm to a real-time robotic learning problem, where the goal is to maximize the efficiency of lift generation on a dynamically scaled flapping robotic wing. The robotic wing has two degrees-of-freedom, i.e., stroke and pitch, and operates in a tank filled with mineral oil. For all experiments, the Reynolds number is maintained constant at 1000, where learning is performed for different prescribed stroke amplitudes to find the optimal wing pitching amplitude and the stroke-pitch phase difference that maximize the power loading (PL) of lift generation, a measure of aerodynamic efficiency. For the investigated stroke amplitude range (30°-90°), the efficiency is observed to increase with the stroke amplitude and the lift is mainly generated through the delayed stall, a quasi-steady aerodynamic mechanism. Furthermore, the wing rotation becomes more asymmetric with respect to stroke reversal as the stroke amplitude decreases, indicating an increased use of unsteady lift generation mechanisms at lower stroke amplitudes.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5519-5525
Number of pages7
ISBN (Electronic)9781538630815
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
CountryAustralia
CityBrisbane
Period5/21/185/25/18

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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