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 | |
| Publication status | Published - 25 Jul 2019 |
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Keywords
- unmanned aerial vehicle
- neural network
- soft landing gear
- aerospace
- design
Cite this
Luo, C., Zhao, W., Du, Z., & Yu, L. (2019). A neural network based landing method for an unmanned aerial vehicle with soft landing gears. Applied Sciences, 9(15), [2976]. https://doi.org/10.3390/app9152976