A Research Platform for Flapping Wing Micro Air Vehicle Control Study

Botha, Hermanus V.; Boddhu, Sanjay K.; McCurdy, Helena B.; Gallagher, John C.; Matson, Eric T.; Kim, Yong-Ho

doi:10.1007/978-3-319-16841-8_14

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…A conceptual vehicle closely related to those described by Wood and Doman et al is presented in [20]. The physical analogue descendant from it is described in [17][18][19]. Both vehicles operate in a qualitatively similar manner with two minimally actuated wings providing all propulsion and control forces.…”

Section: Vehicle Configurationmentioning

confidence: 99%

“…The vehicle employed in this research is a hardware analogue of a minimally-actuated flapping-wing vehicle introduced by Wood [13,14] with core control laws introduced and subsequently refined by Doman et al [15,16]. The analogue vehicle [17][18][19] operates similarly to the minimally actuated vehicles considered by Wood and Doman et al in that all propulsion and control are provided by two minimally actuated wings, each of which possesses a single active and a single passive degree of freedom. It differs in that the analogue vehicles active degrees of freedom are driven by a DC motor through a four-bar linkage instead of a piezoelectric transducer and in that the analogue vehicle is mounted vertically on a circular puck and supported from below by either air or fluid cushion.…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary Approach to Tuning a Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle

Podhradský

Greenwood

2016

Int. J. Robot. Autom. Technol.

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Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper, we describe an evolutionary approach to tuning a Multi-Agent System for autonomous adaptive control of a Flapping-Wing Micro Air Vehicle. The wings of the vehicle are controlled by a split cycle oscillator, which combined with non-linearities and differences between each vehicle, brings significant challenge for selecting the proper parameters for the control system. Adopting a Neo-Darwinistic evolutionary approach, where solutions are evolved in a similar manner as in nature, allows us to precisely learn control parameters for each vehicle. After describing the evolution algorithm and evolving the control parameters, we utilize these values for autonomous waypoint following by the micro air vehicle.

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