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Because of its unique system features, it is very difficult for small rotor UAVs to be modeled. In this paper, the method of unmanned aerial vehicle modeling is summarized in detail. In order to achieve a good control effect, the accuracy of the model is very important for the design and verification of the control law. In this paper, the linear model and nonlinear model identification method and identification algorithm of unmanned aerial vehicle are proposed. The simulation results show that the attitude control precision is improved effectively. Introduction Compared to its full-size counterpart, Unmanned Aerial Vehicle (UAV) is much cheaper, easier to carry and more agile, and especially the rotary-wing class, with its ability of hovering, taking off and landing vertically, is very suitable for flying in low-altitude and cramped space, so it is expected to be used widely both in military and civilian domain in the future. The Identification Model of UAV The Linear Model of UAV. The dynamics of UAV are divided into horizontal dynamic (attitude dynamics), vertical dynamic and yaw dynamics. The main rotor's swing has a relatively large dynamic impact on the horizontal. When the vehicle is hovering, its dynamic behavior can be expressed as two first order models coupled vertically and horizontally.

Research on the Identification Method and Modeling of Unmanned Aerial Vehicle based on Neural Network