Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

Steven Aurecianus,Hoon-Cheol Park,Gi-Heon Ha,Tae-Sam Kang

doi:10.3390/app12052486

Steven Aurecianus, Hoon-Cheol Park + Show 2 more

Open Access

https://doi.org/10.3390/app12052486

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Journal: Applied Sciences	Publication Date: Feb 27, 2022
Citations: 3	License type: CC BY 4.0

Affiliation: Konkuk University

Abstract

In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.

Highlights

The flapping stroke plane of insects is nearly horizontal, which causes similar lift force to be produced during a downstroke and an upstroke, whereas the flapping stroke plane of birds is practically vertical, which causes most of the lift force to be produced during downstroke [23,24]
These flight characteristics are mimicked by flapping-wing micro air vehicle (FWMAV) using various approaches and engineering designs, especially for producing wing flapping motion and other kinematics to generate force and moment [19,20,26]
The fabrication of tailless FWMAVs with successful flight makes it possible for system identification based on measured input and output data to be conducted [65,76–82]

Summary

Introduction

Nature provides investigators with abundant insights and inspirations that aid in solving engineering problems and creating new inventions [1,2]. The flapping stroke plane of insects is nearly horizontal, which causes similar lift force to be produced during a downstroke and an upstroke, whereas the flapping stroke plane of birds is practically vertical, which causes most of the lift force to be produced during downstroke [23,24] These flight characteristics are mimicked by FWMAVs using various approaches and engineering designs, especially for producing wing flapping motion and other kinematics to generate force and moment [19,20,26]. The system identification of a tailless FWMAV called KUBeetle was conducted to improve the previously derived longitudinal mode extended dynamic model [34] and obtain a more accurate dynamic model to design a controller. The fabrication of tailless FWMAVs with successful flight makes it possible for system identification based on measured input and output data to be conducted [65,76–82] This approach can potentially identify real dynamics and address the design changes of tailless FWMAVs to obtain a dynamic model with a high degree of similarity to actual systems. While relaxing parameter restrictions might improve the result of system identification, some refined parameters might not match the real physical system

Experimental Flight and System Identification

Experimental Flight Setup

DDaattaa AAcqusition for System Identiffiication via Experimental Flight

Method

Identification Method GNA

Findings

Dynamic Model Analysis and Verification