Identification and adaptive control of history dependent unsteady aerodynamics for a flapping insect wing

Dadashi, Shirin; Feaster, Jeffrey; Bayandor, Javid; Battaglia, Francine; Kurdila, Andrew J.

doi:10.1007/s11071-016-2768-3

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…In this paper, we have derived an explicit bound for the error of approximation for certain history dependent operators that are used in construction of robotic FDE's in [35] and this paper. The numerical simulations presented validate our results.…”

Section: Results and Conclusionmentioning

confidence: 99%

“…One of the characteristics that has inspired the approach here is the history dependence of the aerodynamic lift and drag functions. We refer the interested reader to [35] to study this phenomena in detail. Although CFD methods are advantages in several aspects, they suffer from curse of dimensionality which makes them a very unfavorable choice for online control applications.…”

Section: History Dependent Operatorsmentioning

confidence: 99%

“…These fundamental kernels, each of which has simple input-output relationships, play the role of building blocks for modeling much more complex response characteristics. See [32] for studies of history dependent active materials, [35] for applications that represent nonlinear aerodynamic loading, or Section 6 of this paper to see an example of richness of this class of models.…”

Section: Approximation Of History Dependent Operatorsmentioning

confidence: 99%

“…We can exploit the model in the numerical simulations to perform an online estimation of the history dependent aerodynamics and adaptive control of a simple wing model. The details of offline identification of history dependent aerodynamics follow the steps explained in [35].…”

Section: Operator Approximation Errormentioning

confidence: 99%

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Online estimation and adaptive control for a class of history dependent functional differential equations

2018

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This paper presents sufficient conditions for the convergence of online estimation methods and the stability of adaptive control strategies for a class of history dependent, functional differential equations. The study is motivated by the increasing interest in estimation and control techniques for robotic systems whose governing equations include history dependent nonlinearities. The functional differential equations in this paper are constructed using integral operators that depend on distributed parameters. As a consequence the resulting estimation and control equations are examples of distributed parameter systems whose states and distributed parameters evolve in finite and infinite dimensional spaces, respectively. Well-posedness, existence, and uniqueness are discussed for the class of fully actuated robotic systems with history dependent forces in their governing equation of motion. By deriving rates of approximation for the class of history dependent operators in this paper, sufficient conditions are derived that guarantee that finite dimensional approximations of the online estimation equations converge to the solution of the infinite dimensional, distributed parameter system. The convergence and stability of a sliding mode adaptive control strategy for the history dependent, functional differential equations is established using Barbalat's lemma.

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Section: Results and Conclusionmentioning

confidence: 99%

Section: History Dependent Operatorsmentioning

confidence: 99%

Section: Approximation Of History Dependent Operatorsmentioning

confidence: 99%

Section: Operator Approximation Errormentioning

confidence: 99%

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Online estimation and adaptive control for a class of history dependent functional differential equations

2018

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“…The advances in computational modeling and resulting aerodynamic understanding of complex flight phenomenon is directly aiding the development of robotic MAV systems ( Wood et al, 2013 ). There have been multiple attempts to create low-order models approximating more complex computational fluid dynamics (CFD) results, which could be implemented into various control schemes ( Amiralaei et al, 2012 ; Singh et al, 2004 ; Taha et al, 2014 ; Bayandor et al, 2013 ; Dadashi et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A computational study on the influence of insect wing geometry on bee flight mechanics

2017

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Two-dimensional computational fluid dynamics (CFD) is applied to better understand the effects of wing cross-sectional morphology on flow field and force production. This study investigates the influence of wing cross-section on insect scale flapping flight performance, for the first time, using a morphologically representative model of a bee (Bombus pensylvanicus) wing. The bee wing cross-section was determined using a micro-computed tomography scanner. The results of the bee wing are compared with flat and elliptical cross-sections, representative of those used in modern literature, to determine the impact of profile variation on aerodynamic performance. The flow field surrounding each cross-section and the resulting forces are resolved using CFD for a flight speed range of 1 to 5 m/s. A significant variation in vortex formation is found when comparing the ellipse and flat plate with the true bee wing. During the upstroke, the bee and approximate wing cross-sections have a much shorter wake structure than the flat plate or ellipse. During the downstroke, the flat plate and elliptical cross-sections generate a single leading edge vortex, while the approximate and bee wings generate numerous, smaller structures that are shed throughout the stroke. Comparing the instantaneous aerodynamic forces on the wing, the ellipse and flat plate sections deviate progressively with velocity from the true bee wing. Based on the present findings, a simplified cross-section of an insect wing can misrepresent the flow field and force production. We present the first aerodynamic study using a true insect wing cross-section and show that the wing corrugation increases the leading edge vortex formation frequency for a given set of kinematics.

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An Adaptive Neural Identifier with Applications to Financial and Welding Systems

Gularte

Chávez

Vargas

et al. 2021

Int. J. Control Autom. Syst.

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Identification and adaptive control of history dependent unsteady aerodynamics for a flapping insect wing

Cited by 6 publications

References 34 publications

Online estimation and adaptive control for a class of history dependent functional differential equations

Online estimation and adaptive control for a class of history dependent functional differential equations

A computational study on the influence of insect wing geometry on bee flight mechanics

An Adaptive Neural Identifier with Applications to Financial and Welding Systems

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