基于神经网络和干扰观测器的UAV自动着舰控制

doi:10.7527/S1000-6893.2022.26963

Abstract

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:

V249

HU Wei, WAN Wenzhang, CHEN Mou. Neural network and disturbance observer based control for automatic carrier landing of UAV[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726963-726963.

References

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

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