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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (S1): 726963-726963.doi: 10.7527/S1000-6893.2022.26963

• Swarm Intelligence and Cooperative Control • Previous Articles     Next Articles

Neural network and disturbance observer based control for automatic carrier landing of UAV

HU Wei1, WAN Wenzhang2, CHEN Mou1   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. AVIC Shenyang Aircraft Design and Research Institute, Shenyang 110035, China
  • Received:2022-01-18 Revised:2022-01-28 Published:2022-03-04
  • Supported by:
    Key Research and Development Projects (Social Development) in Jiangsu Province of China (BE2020704); Aeronautical Science Foundation of China (20200007052001)

Abstract: An automatic carrier landing control method based on the neural network and disturbance observer is proposed for the Unmanned Aerial Vehicle (UAV) with dynamic disturbance. The neural network is employed to deal with system uncertainty, and the disturbance observer is employed to estimate the external disturbance of the UAV. By using the neural network output and disturbance observer output, a nonlinear automatic carrier landing controller for the UAV is designed to realize effective tracking of the landing control command. Utilizing the Lyapunov stability method, the convergence of all the closed-loop signals is proved strictly, ensuring the precise automatic carrier landing control of the UAV. Simulation results show the effectiveness of the proposed control method.

Key words: shipboard unmanned aerial vehicle, flight control, automatic carrier landing control, neural network, disturbance observer

CLC Number: