Dynamics of hang-gliders

Matteis, G. De

doi:10.2514/3.20769

Cited by 17 publications

(16 citation statements)

References 3 publications

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“…The flexible wing is connected to the fuselage through a control bar. The aerodynamic forces are controlled via a weight shift mechanism, where the fuselage's center of gravity "floats" with respect to that of the wing [8][9][10][11][12]37,44]. Such a system is governed by complex aerodynamic forces which makes it difficult to model to a satisfactory accuracy.…”

Section: Control Mechanism Of a Flexible Wing Aircraftmentioning

confidence: 99%

“…The ADP approaches that consider gradient-based forms provide fast converging approaches, but they require the complete knowledge of the dynamical model of the system under consideration [7]. The solution of the flexible wing control problem requires model-free approaches, since the aerodynamics of the flexible wing aircraft are highly nonlinear and they variate continuously [8][9][10][11][12][13][14][15][16]. This type of aircraft has large uncertainties embedded in their aerodynamic models.…”

Section: Introductionmentioning

confidence: 99%

“…An approximate modeling approach of the flexible wing's aerodynamics led to equations of motion for the lateral and longitudinal directions with small perturbation models in [42]. The modeling process for the hang glider assumed a rigid wing modeling process, where the derivatives, due to the aerodynamics, were added at the last stage [11,12]. A comprehensive decoupled aerodynamic model for the hang glider is presented in [43].…”

Section: Introductionmentioning

confidence: 99%

“…The reduced center of gravity magnifies the static pitch stability [9]. Frequency response-based approaches are adopted to study the stability of flexible wing systems in [11,12]. The longitudinal stability of a fixed wing system can be used to understand that of the flexible wing vehicle provided some conditions are satisfied [37].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

2018

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Classical gradient-based approximate dynamic programming approaches provide reliable and fast solution platforms for various optimal control problems. However, their dependence on accurate modeling approaches poses a major concern, where the efficiency of the proposed solutions are severely degraded in the case of uncertain dynamical environments. Herein, a novel online adaptive learning framework is introduced to solve action-dependent dual heuristic dynamic programming problems. The approach does not depend on the dynamical models of the considered systems. Instead, it employs optimization principles to produce model-free control strategies. A policy iteration process is employed to solve the underlying Hamilton–Jacobi–Bellman equation using means of adaptive critics, where a layer of separate actor-critic neural networks is employed along with gradient descent adaptation rules. A Riccati development is introduced and shown to be equivalent to solving the underlying Hamilton–Jacobi–Bellman equation. The proposed approach is applied on the challenging weight shift control problem of a flexible wing aircraft. The continuous nonlinear deformation in the aircraft’s flexible wing leads to various aerodynamic variations at different trim speeds, which makes its auto-pilot control a complicated task. Series of numerical simulations were carried out to demonstrate the effectiveness of the suggested strategy.

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Section: Control Mechanism Of a Flexible Wing Aircraftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

2018

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“…The rigid wing exhibits more linear pitching moment characteristics at greater angles of attack as compared to flexible wings [15,16,53]. to the additional drag that it generates.…”

Section: Pitching Moment Coefficientmentioning

confidence: 99%

A Wind-Tunnel Investigation of an Ultra-Light Wing and Ultra-Light Aircraft

Khaddage¹

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A wind-tunnel investigation was undertaken on a scaled down rigid model of both an ultra-light wing and an ultra-light aircraft. The results of the ultra-light wing were compared to an existing computation fluid dynamics (CFD) simulation for validation purposes. The comparisons indicated that the results of the simulation did not agree with those from experimentation; it is most notably observed in the drag coefficient results as they are clearly erroneous and further work is required on developing a validated simulation. The basic performance characteristics along with a longitudinal and lateral static stability analysis were completed. It was observed that the aircraft's drag polar does not conform to a classic parabolic shape commonly used to describe conventional fixed-wing aircraft; the suspended fuselage was found to have a dominant effect on the shape of the drag polar when compared to the wing-only experiments. Furthermore, the aircraft was found to be statically stable in pitch and statically unstable in yaw. The pitch stiffness, c Mα , was determined to be -8.33 rad −1 . The weathercock stability derivative, c N β , was determined to be -0.0274 rad −1 implying directional instability.ii

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Online model-free controller for flexible wing aircraft: a policy iteration-based reinforcement learning approach

Abouheaf

Gueaieb

2019

Int J Intell Robot Appl

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The aerodynamic model of flexible wing aircraft is highly nonlinear with continuously time-varying dynamics under kinematic constraints. The nonlinearities stem from the aerodynamic forces and continuous deformations in the flexible wing. In spite of the various experimental attempts and theoretical setups that were made to model these dynamics, an accurate formulation was not achieved. The control paradigms of the aircraft are concerned with the electro-mechanical coupling between the pilot and the wing. It is challenging to design a flight controller for such aircraft while complying with these constraints. In this paper, innovative machine learning technique is employed to design a robust online model-free control scheme for flexible wing aircraft. The controller maintains internal asymptotic stability for the aircraft in real-time using selected set of measurements or states in uncertain dynamical environment. It intelligently incorporates the varying dynamics, geometric parameters, and physical constraints of the aircraft into optimal control strategies. The adaptive learning structure employs a policy iteration approach, taking advantage of Bellman optimality principles, to converge to an optimal control solution for the problem. Artificial neural networks are adopted to implement the adaptive learning algorithm in real-time without prior knowledge of the aerodynamic model of the aircraft. The control scheme is generalized and shown to function effectively for different pilot/wing control mechanisms. It also demonstrated its ability to overcome the undesired stability problems caused by coupling the pilot's dynamics with the flexible wing's frame of motion.

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Dynamics of hang-gliders

Cited by 17 publications

References 3 publications

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

A Wind-Tunnel Investigation of an Ultra-Light Wing and Ultra-Light Aircraft

Online model-free controller for flexible wing aircraft: a policy iteration-based reinforcement learning approach

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