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UDC 517.97:629.1

ALGORITHMS DESIGN FOR THE CONTROLLED SYSTEMS PARAMETERS MODELS VALUES SEARCH TAKING INTO ACCOUNT THE FUEL CONSUMPTION MINIMALITY CONDITION

О. V. Druzhinina, PhD (physics and mathematics sciences), full professor, chief researcher, FRC CSC RAS, ICS RAS, Moscow; This email address is being protected from spambots. You need JavaScript enabled to view it.
O. N. Masina, PhD (physics and mathematics sciences), аssociate professor, Head of the Department, Bunin Yelets State University, Yelets; This email address is being protected from spambots. You need JavaScript enabled to view it.
A. A. Petrov, post-graduate student, Bunin Yelets State University, Yelets; This email address is being protected from spambots. You need JavaScript enabled to view it.

The problems of the optimal motion parameters search for some classes of technical systems, modelled by differential inclusions are considered. The aim of the work is the design of generalized mathematical models of studied systems, the subsequent approximation of the models using the methods of intelligent control, and the elaboration of the optimal parameters search algorithms of the controlled systems models taking into account the fuel consumption minimality condition. Interpretation of computing experiments on the basis of the created software package is given. The initial two-dimensional model without taking into account the variability of the motion endpoint is generalized for cases of three and six variables. The optimal trajectory for the three-dimensional model taking into account the variability of the endpoint position and multivaluence is found and also numerical optimization algorithms based on a modified gradient descent method and evolutionary computations are detailed. The developed algorithms can find application in designing the problems of the technical devices, in the motion control problems of unmanned aerial vehicles, in robotics.

Key words: mathematical modeling, controlled technical systems, differential inclusions, variability, multivaluence, optimization, algorithms, artificial neural networks, gradient descent method.

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