A neural network based landing method for an unmanned aerial vehicle with soft landing gears

Cai Luo; Weikang Zhao; Zhenpeng Du; Leijian Yu

doi:10.3390/app9152976

A neural network based landing method for an unmanned aerial vehicle with soft landing gears

Cai Luo, Weikang Zhao, Zhenpeng Du, Leijian Yu

Design, Manufacturing And Engineering Management

Research output: Contribution to journal › Article

3 Citations (Scopus)

46 Downloads (Pure)

Abstract

This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.

Original language	English
Article number	2976
Number of pages	16
Journal	Applied Sciences
Volume	9
Issue number	15
DOIs	https://doi.org/10.3390/app9152976
Publication status	Published - 25 Jul 2019

Keywords

unmanned aerial vehicle
neural network
soft landing gear
aerospace
design

Access to Document

10.3390/app9152976Licence: CC BY 4.0

Luo-etal-AS-2019-A-neural-network-based-landing-method-for-an-unmanned-aerial-vehicleFinal published version, 7.03 MBLicence: CC BY 4.0

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Cite this

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abstract = "This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.",

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Y1 - 2019/7/25

N2 - This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.

AB - This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.

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KW - aerospace

KW - design

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