Longitudinal modelling and control of a flapping-wing micro aerial vehicle

Rakotomamonjy, Thomas; Ouladsine, Mustapha; Moing, Thierry Le

doi:10.1016/j.conengprac.2010.02.002

Cited by 17 publications

(12 citation statements)

References 10 publications

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“…The first is based on models of the flapping flight including aerodynamics, body and wing dynamics (Deng et al 2006a;Rakotomamonjy et al 2010;Oppenheimer et al 2010;Couceiro et al 2012). Note that well developed models of flapping flight do not exist because the aerodynamics at low Reynolds numbers have not been yet perfectly identified (Dudley 2002).…”

Section: Simulations and Robustness Testsmentioning

confidence: 99%

“…The wing reacts to this phenomenon by creating additional rotational circulation (Sane 2003) and consequently a rotational force modeled by (Rakotomamonjy et al 2010):…”

Section: Rotational Forcementioning

confidence: 99%

“…Models of FMAVs existing in literature consider them as rigid bodies subject to external forces and torques generated by the flapping wings whose amplitudes may be modulated (Deng et al 2006a;Rakotomamonjy et al 2010), phase or even both of them (Chung & Dorothy 2010;Oppenheimer et al 2010;Couceiro et al 2012). Few works have treated the problem of controlling the orientation of flapping aerial vehicles based on sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

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Biomimetic-based output feedback for attitude stabilization of a flapping-wing micro aerial vehicle

Rifaï¹,

Guerrero-Castellanos

Marchand

et al. 2013

Robotica

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The paper deals with the development of a bounded control law for Flappingwing Micro Aerial Vehicles (FMAVs) that mimics a strategy adopted by animal flapping flyers to stabilize their orientation. The control consists on generating torques about the body's principal axes by means of a modulation of the wing angle amplitudes. It is known that flapping flyers orient their body without any numerical computation or estimation of their current attitude. Therefore, the proposed control law is computed using the direct measurements of on-board sensors mimicking animal sensitive organs, more specifically the halteres, legs sensilla and magnetic sense. The technological equivalents of these biological sensors are respectively three rate gyros, a tri-axis accelerometer and a tri-axis magnetometer. Besides, the control signal is bounded to keep the wing angle amplitudes below the maximal values. Owing to its simplicity, this control law is suitable for applications where on-board computational resources are limited. The stability of the closed loop system is proved based on Lyapunov analysis and averaging theory. The effectiveness of the proposed control law is shown in simulations. The robustness with respect to external disturbances is also shown emphasizing the importance and need of the bounded control.

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Section: Simulations and Robustness Testsmentioning

confidence: 99%

“…The wing reacts to this phenomenon by creating additional rotational circulation (Sane 2003) and consequently a rotational force modeled by (Rakotomamonjy et al 2010):…”

Section: Rotational Forcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Biomimetic-based output feedback for attitude stabilization of a flapping-wing micro aerial vehicle

Rifaï¹,

Guerrero-Castellanos

Marchand

et al. 2013

Robotica

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“…Due to the lack of knowledge about the fundamental flow physics within this regime, many dynamic effects might not be reflected with the current flapping wing model, which could causes failures during practical implementation [8,9]. To neutralize any such conflict, a novel Neural-Immunology/Memory Network is proposed in this work.…”

Section: Introductionmentioning

confidence: 99%

Micro Aerial Vehicle (MAV) Flapping Motion Control Using an Immune Network with Different Immune Factors

Weng

Xia

et al. 2013

International Journal of Advanced Robotic Systems

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This paper proposes a novel NeuralImmunology/Memory Network to address the problem of motion control for flapping-wing Micro Aerial Vehicles (MAVs). This network is inspired by the human memory system as well as the immune system, and it is effective in attenuating the system errors and other lumped system uncertainties. In contrast to most existing Neural Networks, the convergence of this proposed NeuralImmunology/Memory Network can be theoretically proven. Both analyses and simulations that are based on different immune factors show that the proposed control method is effective in dealing with external disturbances, system nonlinearities, uncertainties and parameter variations.

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“…The possibility of having actuated wings has allowed the design of new mechanisms that improve over classical fixed/rotary-wings MAV flight performance. As a result, different morphing-wing concepts and materials have emerged together with control methodologies that allow for accurate wing-actuation [4], [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Inertial attitude control of a bat-like morphing-wing air vehicle

Colorado¹,

Barrientos²,

Rossi³

et al. 2012

Bioinspir. Biomim.

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Abstract.This article presents a novel bat-like micro air vehicle inspired by the morphing-wing mechanism of bats. The goal of this paper is twofold. Firstly, a modelling framework is introduced for analysing how the robot should maneuver by means of changing wing morphology. This allows the definition of requirements for achieving forward and turning flight according to the kinematics of the wing modulation. Secondly, an attitude controller named backstepping+DAF is proposed. Motivated by the biological fact about the influence of wing inertia on the production of body accelerations, the attitude control law incorporates wing inertia information to produce desired roll (φ) and pitch (θ) acceleration commands (DAF function). This novel control approach is aimed at incrementing net body forces (F net ) that generate propulsion. Simulations and wind-tunnel experimental results have shown an increase about 23% in net body force production during the wingbeat cycle when the wings are modulated using the DAF function as a part of the backstepping control law. Results also confirm accurate attitude tracking in spite of high external disturbances generated by aerodynamic loads at airspeeds up to 5ms −1 . Inertial Attitude Control of a Bat-like Morphing-wing Micro Air Vehicle2

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Longitudinal modelling and control of a flapping-wing micro aerial vehicle

Cited by 17 publications

References 10 publications

Biomimetic-based output feedback for attitude stabilization of a flapping-wing micro aerial vehicle

Biomimetic-based output feedback for attitude stabilization of a flapping-wing micro aerial vehicle

Micro Aerial Vehicle (MAV) Flapping Motion Control Using an Immune Network with Different Immune Factors

Inertial attitude control of a bat-like morphing-wing air vehicle

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