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

Cai Luo, Zhenpeng Du, Leijian Yu

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
3 Downloads (Pure)


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.
Original languageEnglish
Article number931
Number of pages16
Issue number9
Publication statusPublished - 1 Sep 2019


  • quadrotor helicopters
  • transportation system
  • neural network


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