METHOD OF THE INTELLIGENT SYSTEM CONSTRUCTION OF AUTOMATIC CONTROL OF UNMANNED AIRCRAFT APPARATUS
DOI:
https://doi.org/10.15588/1607-3274-2019-1-20Keywords:
automatic control intellectual system, navigation system, unmanned aircraft apparatus.Abstract
Context. Military conflicts of the late XX – early XXI centuries are characterized by the using of a large number of new weapons, which allowed the warring parties to distance themselves as far as possible from the direct collision with each other. Unmanned aircraft apparatus (UAA) have become one of the latest weapons on the battlefield, which during military conflicts were proven to be more effective than manned planes, in conducting air reconnaissance and other combat tasks, as well as strike at the enemy. One of the ways to increase the efficiency of UAA is to increase the level of technical excellence of their control systems. Creating new approaches for designing navigation systems for unmanned aerial vehicles particular, based on a free-form inertial navigation system, is an urgent task, as it will allow automatic control of the UAA flight route in the absence of corrective signals from the global satellite navigation system.
Objective. The purpose of this work is to develop a methodology for managing an unmanned aerial apparatus using an intelligent automatic control system. This technique will minimize the error of a free inertial navigation system due to the using of a fuzzy neural network system. The algorithm of the proposed method of constructing the intellectual system of automatic control of UAA navigation
system using the fuzzy neural network apparatus in the MatLab 7 software environment was developed. A neural network training was conducted in the Python 3.6 software environment (Jupyter-notebook), as well as testing the UAA model in the robot operational system (ROS) simulator environment for comparison with existing methods.
Method. To achieve this goal, the following methods were used: intelligent systems, the theory of automatic control, pseudospectral method; methods based on the genetic algorithm and apparatus of the fuzzy neural network.
Results. The method of constructing the intelligent system of automatic control of an unmanned aerial apparatus for minimizing the error of a free-form inertial navigation system due to the application of the neural network has been developed. The work of the intellectual system of automatic control of the UAA navigational system using the neural network in the MatLab software environment
based on the proposed implementation algorithm were tested. The possibility of practical application of the obtained results and comparison with traditional methods were investigated. Conclusions. The technique of the intelligent automatic control of UAA shows an advantage in comparison with the known methods without correcting signals from the global navigation satellite system.
References
Information technology. Vocabulary. Part 28. Artificial
intelligence. Basic сoncepts and expert systems : ISO/IEC
–28:1995. [Effective from 1995-12-15]. Geneve, 1995,
p.
Avtomatyzovani systemy. Terminy ta vyznachennja : DSTU
–93. [Chynnyj vid 1993-04-01]. Kyev, Derzhstandart
Ukrai’ny, 1993, 86 p. (Nacional’nyj standart Ukrai’ny).
Subbotin S. O., Olijnyk A. O. (Ukrai’na). Pat. 18294
Ukrai'na, MPK2006 G06F 19/00. Sposib vidboru
informatyvnyh oznak dlja diagnostyky vyrobiv / zajavnyk
Zaporiz'kyj nacional'nyj tehnichnyj universytet. №
u200603087; Zajavl. 22.03.06; Opubl.15.11.06, Bjul. №11.
p.
Akaike H. A new look at the statistical model identification,
IEEE Transactions on Automatic Control, 1974, Vol. 19, No. 6,
pp. 716–723.
Babak O. V., Tatarinov A. E’. Ob odnom podxode k
resheniyu zadach klassifikacii v usloviyax nepolnoty
informacii, Kibernetika i sistemnyj analiz, 2005, No. 6,
pp. 116–123.
Slyusar V. I. Voyennaya svyaz’ stran NATO: problemy
sovremennykh tekhnologiy, Elektronika: Nauka,
Tekhnologiya, Biznes, 2008, No. 4, pp. 66–71.
Slyusar V. I. Peredacha dannykh s borta BPLA: standarty
NATO, Elektronika: nauka, tekhnologiya, biznes, 2010,
No. 3, pp. 80–86.
Slyusar V. I. Radiolinii svyazi s BPLA: primery realizatsii,
Elektronika: nauka, tekhnologiya, biznes, 2010, No. 5,
pp. 56–60.
Romanyuk V. A., Stepanenko Ê. O., Panchenko Í. V.,
Voskolovich O. Í. Lítayuchí samoorganízuyuchí
radíomerezhí, Zbírnik naukovikh prats’ VÍTÍ, 2017, No. 1,
pp. 104–114.
Romanyuk V. A., Stepanenko Ê. O. Zadachí sintezu
topologíy merezh mobíl’noí komponenti z vikoristannyam
telekomuníkatsíynikh ayeroplatform, Zbírnik naukovikh
prats’ VÍTÍ, 2017, No. 3, pp. 149–157.
Suyi L., Wang S. Machine health monitoring and
prognostication via vibration information, Intelligent
systems design and applications : Sixth international
conference, Jinan, 16–18 October 2006 : proceedings. Los
Alamitos, IEEE, 2006, P. 879.
Subbotin S. O. Programni zasoby syntezu diagnostychnyh i
rozpiznaval’nyh modelej za precedentamy, Suchasni
problemy i dosjagnennja v galuzi radiotehniky,
telekomunikacij ta informacijnyh tehnologij : VI
Mizhnarodna naukovo-praktychna konferencija,
Zaporizhzhja, 19–21 veresnja 2012 r. : tezy dopovidej.
Zaporizhzhja, ZNTU, 2012, P. 21–22.
Neagu C.-D. Using artificial neural networks in fuzzy
reasoning : abstract of the dissertation ... doctor of
philosophy in computer science. Galati, University of Galati,
, 42 p.
Snytjuk V. Je. Evoljucijni tehnologii’ pryjnjattja rishen’ v
umovah nevyznachenosti : avtoref. dys. ... d-ra tehn. nauk :
13.06 / NAN Ukrai’ny; Instytut problem matematychnyh
mashyn i system. Kyiv, 2009, 36 p.
Li S. Automated tool condition monitoring in machining
using fuzzy neural networks : thesis doctor of Ph. Hamilton,
McMaster University, 1995, 187 p.
Voronkin R. A. Matematicheskoe modelirovanie processov
geneticheskogo poiska dlya povysheniya kachestva
obucheniya nejronnyx setej pryamogo rasprostraneniya : dis.
kand. texn. nauk : 05.13.18. Stavropol’, 2004, 237 p.
Abraham A., Grosan C., Pedrycz W.. Engineering
evolutionary intelligent systems. Berlin, Springer, 2008,
p.
Boguslaev A. V., Olejnik Al. A., Olejnik An. A.,
Pavlenko D. V., Subbotin S. A.; pod red. D. V. Pavlenko,
S. A. Subbotina. Progressivnye texnologii modelirovaniya,
optimizacii i intellektual’noj avtomatizacii e’tapov
zhiznennogo cikla aviacionnyx dvigatelej : monografiya.
Zaporozh’e, OAO “Motor Sich”, 2009, 468 p.
Klyuev V. V., Sosnin F. R., Filinov V. N. i dr.; pod obshh.
red. V. V. Klyueva. Mashinostroenie : e’nciklopediya / ped.
sovet: K.V. Frolov (pred.) i dr. Moscow, Mashinostroenie,
Vol. III-7, Izmereniya, kontrol’, ispytaniya i diagnostika,
, 464 p.
UCI machine learning repository [Electronic resource].
Access mode: http://archive.ics.uci.edu/ml/datasets/
Shuvakin Yu. A. Simulation of the kinematics and flight
dynamics of an unmanned aerial vehicle, Problems of
modern science and education: Olympus. Ivanovo, 2016,
No. 16 (58), pp. 44–47.
Lebedev A. A., Chernobrovkin L. S. under redaction of
V. K. Salnik Dynamics of flight of unmanned aerial
vehicles: Textbook for High Schools. Moscow, 2010, 618 p.
Chernodub A. N. Training of neuroemulators with use of
pseudoregularization for model reference adaptive
neurocontrol, Intellеktual'nye systemy, 2012, No. 4, pp. 602–
Mu C., Wang D. Neural-network-based adaptive guaranteed
cost control of nonlinear dynamical systems with matched
uncertainties, Neurocomputing, 2017, Vol. 245, pp. 46–54.
Lin Z., Ma D., Meng J., Chen L. Relative ordering learning
in spiking neural network for pattern recognition,
Neurocomputing, 2018, Vol. 275, pp. 94–106.
Yu J., Sang J., Gao X. Machine learning and signal
processing for big multimedia analysis, Neurocomputing,
, Vol. 257, pp. 1–4.
Lv Y., Na J., Yang Q., Wu X., Guo Y. Online adaptive
optimal control for continuous-time nonlinear systems with
completely unknown dynamics, International Journal of
Control, 2016, Vol. 89, pp. 99–112.
Sun Y., Xue B., Zhang M., Yen G. G. Automatically
Designing CNN Architectures Using Genetic Algorithm for
Image Classification, Cornell University Libreri, Electronic
data, 2018. Mode of access:
https://arxiv.org/abs/1808.03818 (viewed on Aug 11, 2018).
Title from the screen.
Zela A., Klein A., Falkner S., Hutter F. Towards Automated
Deep Learning: Efficient Joint Neural Architecture and
Hyperparameter Search, Cornell University Libreri. Electronic
data, 2018. Mode of access:
https://arxiv.org/abs/1807.06906 (viewed on Jul 18, 2018).
Title from the screen.
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Copyright (c) 2019 R. O. Bieliakov, H. D. Radzivilov, O. D. Fesenko, V. V. Vasylchenko, O. G. Tsaturian, A. V. Shyshatskyi, V. P. Romanenko
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