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Algorithm of quadrotor vehicle control based on neural network for typical trajectories


V.Е. Pavlovsky - Dr.Sc. (Phys.-Math.), Professor, Chief Research Scientist, Keldysh Institute of Applied Mathematics of RAS (Moscow). E-mail: A.V. Savitskiy - Post-graduate Student, Lomonosov Moscow State University. E-mail:

This paper presents a theoretical mechanical model of an unmanned aerial vehicle - quadrotor. There are the description of the lifting power and the moment of resistance of the rotor in the first part of this paper. Using the Lagrange equations it was described the dynamics of quadrotors in general. Such basic trajectories as take-off, landing, hovering, flying in a straight line were studied. A convergent neural network algorithm of control was considered. A lot of effects and impacts that are difficult to model mathematically appear in-flight of multirotor robot. The neural network controller solves this type of problems. Neural controller consists of three blocks: the sensor unit, the unit of the neural network, and integrating unit. Based on the results of numerical simulation it was built a training set and а neural network. It is shown that the constructed control algorithm satisfactorily controls the rotation angle in the case of basic trajectories. The results of the neural network algorithm for the case of the presence of random noise and errors in the position sensor were also presented. It is shown that the neural controller works satisfactorily for take-off even if the height sensor has error of 5 cm.


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