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Instruments and Systems: Monitoring, Control, and Diagnostics

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Position-Trajectory Control System for an Unmanned Aerial Vehicle
D.E. RASTREPIN, K.A. NEUSYPIN

An algorithm for neural network trajectory control of the movement of an unmanned aerial vehicle using the deep reinforcement learning method
is considered, which allows landing on a moving platform. An approach was used in the Markov decision-making process model, which evenly takes
into account the importance of all learning episodes. The results of numerical simulation refl ect the effi ciency of trajectory control in the presence of
disturbances.
Keywords: Unmanned aerial vehicle, neural networks, unsupervised learning, reinforcement learning, trajectory control, deep deterministic
gradient algorithm.


DOI: 10.25791/pribor.4.2024.1490

Pp. 25-30.

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