UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR
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For citation: Andropov S.S., Guirik A.V., Budko M.Yu., Budko M.B. Unmanned air vehicle stabilization based on neural network regulator. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 5, pp. 796–800. doi: 10.17586/2226-1494-2016-16-5-796-800
A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.
Acknowledgements. This work was supported by the Russian Science Foundation grant 16-11-00049.
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