Parameter estimation for the pitching dynamics of a flapping-wing flying robot

Chand, Aneesh N.; Kawanishi, Michihiro; Narikiyo, Tatsuo

doi:10.1109/aim.2015.7222763

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…In an application context, the most widely used approach for this is quasi-steady modelling, [8][9][10][11][12] and, to a lesser extent, data-driven modelling. [13][14][15][16][17][18] However, the combination of wings and tail has not been widely studied, despite being used on many flapping-wing robots; instead the design of the tail in such vehicles so far has largely relied on an engineering approach. Dynamic models either consider the vehicle as a whole without distinguishing between tail and wings, 15,18,19 or model the tail separately but without explicitly considering its interaction with the wings.…”

Section: Introductionmentioning

confidence: 99%

Modelling wing wake and tail aerodynamics of a flapping-wing micro aerial vehicle

Armanini

Caetano

Visser

et al. 2019

International Journal of Micro Air Vehicles

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Despite significant interest in tailless flapping-wing micro aerial vehicle designs, tailed configurations are often favoured, as they offer many benefits, such as static stability and a simpler control strategy, separating wing and tail control. However, the tail aerodynamics are highly complex due to the interaction between the unsteady wing wake and tail, which is generally not modelled explicitly. We propose an approach to model the flapping-wing wake and hence the tail aerodynamics of a tailed flapping-wing robot. First, the wake is modelled as a periodic function depending on wing flap phase and position with respect to the wings. The wake model is constructed out of six low-order sub-models representing the mean, amplitude and phase of the tangential and vertical velocity components. The parameters in each sub-model are estimated from stereo-particle image velocimetry measurements using an identification method based on multivariate simplex splines. The computed model represents the measured wake with high accuracy, is computationally manageable and is applicable to a range of different tail geometries. The wake model is then used within a quasi-steady aerodynamic model, and combined with the effect of free-stream velocity, to estimate the forces produced by the tail. The results provide a basis for further modelling, simulation and design work, and yield insight into the role of the tail and its interaction with the wing wake in flapping-wing vehicles. It was found that due to the effect of the wing wake, the velocity seen by the tail is of a similar magnitude as the free stream and that the tail is most effective at 50-70% of its span.

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Section: Introductionmentioning

confidence: 99%

Modelling wing wake and tail aerodynamics of a flapping-wing micro aerial vehicle

Armanini

Caetano

Visser

et al. 2019

International Journal of Micro Air Vehicles

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show abstract

“…Hence, when flight data is obtainable, an attractive alternative to often complex and computationally expensive theoretical or numerical models is system identification. Previous system identification efforts 18,19,20,21,22,23,24 showed that a data-driven approach can yield realistic but simple models, as well as new insight, for a novel and complex type of vehicle, not known in great depth. However relatively limited work has been done in this field so far, partly due to the difficulty of obtaining suitable data.…”

Section: Introductionmentioning

confidence: 99%

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Armanini

Karásek

Visser

et al. 2017

AIAA Atmospheric Flight Mechanics Conference

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“…In [18] it was suggested to use an equivalent ANN model and the sliding modes (SM) in combination with an FF, which gradually reduces the influence of past data. On the other hand, in [19] it is indicated that the FF value should be between zero and one, so that when it is closer to zero, it discards the old data faster, making the response more sensitive to the new data; the opposite happens when the FF is closer to one.…”

Section: Exponential Forgetting Factor (Eff)mentioning

confidence: 99%

Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes

Cruz

Juárez

Parrazales

2016

Computational Intelligence and Neuroscience

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The Artificial Neural Network (ANN) concept is familiar in methods whose task is, for example, the identification or approximation of the outputs of complex systems difficult to model. In general, the objective is to determine online the adequate parameters to reach a better point-to-point convergence rate, so that this paper presents the parameter estimation for an equivalent ANN (EANN), obtaining a recursive identification for a stochastic system, firstly, with constant parameters and, secondly, with nonstationary output system conditions. Therefore, in the last estimation, the parameters also have stochastic properties, making the traditional approximation methods not adequate due to their losing of convergence rate. In order to give a solution to this problematic, we propose a nonconstant exponential forgetting factor (NCEFF) with sliding modes, obtaining in almost all points an exponential convergence rate decreasing. Theoretical results of both identification stages are performed using MATLAB® and compared, observing improvement when the new proposal for nonstationary output conditions is applied.

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Parameter estimation for the pitching dynamics of a flapping-wing flying robot

Cited by 12 publications

References 13 publications

Modelling wing wake and tail aerodynamics of a flapping-wing micro aerial vehicle

Modelling wing wake and tail aerodynamics of a flapping-wing micro aerial vehicle

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes

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