Bounded control of an underactuated biomimetic aerial vehicle—Validation with robustness tests

Rifaï, Hala; Marchand, Nicolas; Poulin‐Vittrant, Guylaine

doi:10.1016/j.robot.2012.05.011

Cited by 15 publications

(6 citation statements)

References 36 publications

(62 reference statements)

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“…They are derived computationally or experimentally with various assumptions, ranging from time-invariant linear to nonlinear time-periodic models [58][59][60]. One of the main purposes of obtaining a dynamic model is to design a flight controller with various control techniques and schemes, such as PID [34,61], linear-quadratic-Gaussian [62], state feedback [63,64], nonlinear [65][66][67], sliding mode [68,69], robust [70,71], adaptive [72], adaptive neural network [73,74], and model-free [75]. While many dynamic models and controllers have been successfully developed, only a limited number have been implemented on tailless FWMAVs because some tailless FWMAVs cannot generate sufficient lift to perform free flight for implementation and verification.…”

Section: Introductionmentioning

confidence: 99%

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

Aurecianus

Park

et al. 2022

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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

Aurecianus

Park

et al. 2022

Applied Sciences

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“…15,16 In order to optimize kinematics, the flight dynamics of the wing and the body models of flapping robots have been utilized in Smith. 17 To estimate the states and synthesis control in typical flapping wings, a multibody model has been suggested in Heathcote et al 18 and Rifai et al 19 Besides these analytical efforts, plenty of the empirical activities have also been carried out in the flapping bird relevant researches. In validating the models as well as determining the empirical relationships between parameters, experimental studies are of great importance, because the aerodynamic of the wing flapping is very complex and with some ambiguity.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic modeling of a flexible flapping-wing micro-air vehicle in the bond graph environment with the aim of assessing the lateral control power

Karimian

Jahanbin

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, an integrated and systematic modeling of all components and subsystems of a flapping bird robot is developed using the bond graph method. The wings kinematic is independently implemented with the aim of evaluating lateral movements of the bird and aerodynamic forces are accurately extracted by assuming quasi-steady theory. In the present dynamic model, the aeroelastic bending behavior of flexible wing of the flapping bird is revealed by the bond graph method. In this regard, three elastic bending modes and one rigid motion mode of the wing are added to the model. In the following, the performance evaluation of the flapping bird and parametric study are carried out around important quantities such as frequency, initial incidence angle, torsional stiffness, and flapping amplitude of the wings, which results in applicable and attractive consequences. As an innovation, the initial classification and presentation of the comparative characteristic curves for generating lateral forces without using an additional control surface and only based on asymmetry in the flapping kinematic using the bond graph approach is done, which is essential to perform lateral-directional maneuvers. Some important correlations are achieved, which are presented in detail in the text. As an example, the relation between lateral force and phase difference between wings is quasi-linear in a variety of velocities. Therefore, this parameter can be used as input in design of lateral control system. On the other hand, the sensitivity of the lateral force to active pitch angle is more than other quantities; as a result, such mechanism can be effectively used in rapid maneuvers. Finally, based on the presented bond graph model, it would be easily possible to study the effects of design variables, mechanical properties, geometric constraints, and specially flapping kinematics on a wide range of functional and performance indices.

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“…Due to the computation simplicity, relatively high fidelity and high compatibility to control dynamic models, many works involving flapping wing aerodynamics implement these methods [13][14][15][16]. Most simulation platforms are first tested with control tasks, meanwhile, the control problem is also the main distinctive part of flapping wing robotic tasks [8][9][10][11][17][18][19]. Thus, we use attitude tracking and trajectory tracking tasks to test the proposed simulation platform, meanwhile, other conventional robotic tasks such as perception and navigation can also be straightforwardly performed.…”

Section: Introductionmentioning

confidence: 99%

Research laboratory for Smart Control Valves at Zhejiang University

Qian

Liu

Zhang

et al. 2019

J. Zhejiang Univ. Sci. A

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Tried-and-true flapping wing robot simulation is essential in developing flapping wing mechanisms and algorithms. This paper presents a novel application-oriented flapping wing platform, highly compatible with various mechanical designs and adaptable to different robotic tasks. First, the blade element theory and the quasi-steady model are put forward to compute the flapping wing aerodynamics based on wing kinematics. Translational lift, translational drag, rotational lift, and added mass force are all considered in the computation. Then we use the proposed simulation platform to investigate the passive wing rotation and the wing-tail interaction phenomena of a particular flapping-wing robot. With the help of the simulation tool and a novel statistic based on dynamic differences from the averaged system, several behaviors display their essence by investigating the flapping wing robot dynamic characteristics. After that, the attitude tracking control problem and the positional trajectory tracking problem are both overcome by robust control techniques. Further comparison simulations reveal that the proposed control algorithms compared with other existing ones show apparent superiority. What is more, with the same control algorithm and parameters tuned in simulation, we conduct real flight experiments on a self-made flapping wing robot, and obtain similar results from the proposed simulation platform. In contrast to existing simulation tools, the proposed one is compatible with most existing flapping wing robots, and can inherently drill into each subtle behavior in corresponding applications by observing aerodynamic forces and torques on each blade element.

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Bounded control of an underactuated biomimetic aerial vehicle—Validation with robustness tests

Cited by 15 publications

References 36 publications

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

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

Aerodynamic modeling of a flexible flapping-wing micro-air vehicle in the bond graph environment with the aim of assessing the lateral control power

Research laboratory for Smart Control Valves at Zhejiang University

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