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Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances.

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances. This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton's principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IEEE transactions on cybernetics Pubmed

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances.

IEEE transactions on cybernetics , Volume 49 (4): 12 – Feb 25, 2019

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances.


Abstract

This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton's principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.

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ISSN
2168-2267
DOI
10.1109/TCYB.2018.2808321
pmid
29994035

Abstract

This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton's principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.

Journal

IEEE transactions on cyberneticsPubmed

Published: Feb 25, 2019

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