A Neural Network Based Landing Method for an Unmanned Aerial Vehicle with Soft Landing Gears

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 helicopte...

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Bibliographic Details
Main Authors: Cai Luo, Weikang Zhao, Zhenpeng Du, Leijian Yu
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/9/15/2976
Description
Summary: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.
ISSN:2076-3417