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

Cai Luo, Zhenpeng Du, Leijian Yu

Research output: Contribution to journalArticle

Abstract

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone's flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers' reliability.
LanguageEnglish
Article number931
Number of pages16
JournalElectronics
Volume8
Issue number9
DOIs
Publication statusPublished - 1 Sep 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
Monitoring
Drones

Keywords

  • quadrotor helicopters
  • transportation system
  • neural network

Cite this

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

In: Electronics, Vol. 8, No. 9, 931, 01.09.2019.

Research output: Contribution to journalArticle

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