Neural network control design for an unmanned aerial vehicle with suspended payload

Cai Luo, Zhenpeng Du, Leijian Yu

Research output: Contribution to journalArticle

Abstract

Unmanned aerial vehicle (UAV) demonstrates excellent maneuverability in clutter environments, which makes it a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenging for a drone with a wire connected package is analyzed. During the delivering tasks, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network based backstepping sliding mode control method is designed 6 which is capable to monitor the drone’s flight path and maintenance desired attitude with suspended cable attached. The convergence of position and attitude errors together with the Lyapunov function are employed to attest the robustness of the nonlinear transportation platform. The proposed control system is tested in a simulation and an outdoor environment. The simulation and open field test results on the UAV transportation platform are demonstrated to verify the controllers’ reliability.
LanguageEnglish
Article number931
Number of pages16
JournalElectronics
Volume8
Issue number9
DOIs
Publication statusPublished - 25 Aug 2019

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Unmanned aerial vehicles (UAV)
Neural networks
Flight paths
Maneuverability
Backstepping
Attitude control
Position control
Sliding mode control
Lyapunov functions
Cables
Wire
Control systems
Controllers
Drones

Keywords

  • quadrotor helicopters
  • transportation system
  • neural network

Cite this

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title = "Neural network control design for an unmanned aerial vehicle with suspended payload",
abstract = "Unmanned aerial vehicle (UAV) demonstrates excellent maneuverability in clutter environments, which makes it a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenging for a drone with a wire connected package is analyzed. During the delivering tasks, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network based backstepping sliding mode control method is designed 6 which is capable to monitor the drone’s flight path and maintenance desired attitude with suspended cable attached. The convergence of position and attitude errors together with the Lyapunov function are employed to attest the robustness of the nonlinear transportation platform. The proposed control system is tested in a simulation and an outdoor environment. The simulation and open field test results on the UAV transportation platform are demonstrated to verify the controllers’ reliability.",
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Neural network control design for an unmanned aerial vehicle with suspended payload. / Luo, Cai; Du, Zhenpeng; Yu, Leijian.

In: Electronics, Vol. 8, No. 9, 931, 25.08.2019.

Research output: Contribution to journalArticle

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AB - Unmanned aerial vehicle (UAV) demonstrates excellent maneuverability in clutter environments, which makes it a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenging for a drone with a wire connected package is analyzed. During the delivering tasks, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network based backstepping sliding mode control method is designed 6 which is capable to monitor the drone’s flight path and maintenance desired attitude with suspended cable attached. The convergence of position and attitude errors together with the Lyapunov function are employed to attest the robustness of the nonlinear transportation platform. The proposed control system is tested in a simulation and an outdoor environment. The simulation and open field test results on the UAV transportation platform are demonstrated to verify the controllers’ reliability.

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