DOI: https://doi.org/10.15588/1607-3274-2019-1-20

METHOD OF THE INTELLIGENT SYSTEM CONSTRUCTION OF AUTOMATIC CONTROL OF UNMANNED AIRCRAFT APPARATUS

R. O. Bieliakov, H. D. Radzivilov, O. D. Fesenko, V. V. Vasylchenko, O. G. Tsaturian, A. V. Shyshatskyi, V. P. Romanenko

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.


Keywords


automatic control intellectual system; navigation system; unmanned aircraft apparatus.

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.


GOST Style Citations


1. Information technology. Vocabulary. Part 28. Artificial
intelligence. Basic сoncepts and expert systems : ISO/IEC
2382-28:1995. – [Effective from 1995-12-15]. – Geneve:
ISO, 1995. – 36 p.
2. Автоматизованi системи. Термiни та визначення : ДСТУ
2226-93. – [Чинний від 1993-04-01]. – К. : Держстандарт
України, 1993. – 86 с. – (Національний стандарт Украї-
ни).
3. Пат. 18294 Україна, МПК 2006 G06F 19/00. Спосіб від-
бору інформативних ознак для діагностики виробів /
С. О. Субботін, А. О. Олійник (Україна); заявник Запо-
різький національний технічний університет. –
№ u200603087; Заявл. 22.03.06; Опубл. 15.11.06,
Бюл. №11. – 4 с.
4. Akaike H. A new look at the statistical model identification /
H. Akaike // IEEE Transactions on Automatic Control. –
1974. – Vol. 19, № 6. – P. 716–723.
5. Бабак О.В. Об одном подходе к решению задач класси-
фикации в условиях неполноты информации / О. В. Ба-
бак, А. Э. Татаринов // Кибернетика и системный ана-
лиз. – 2005. – № 6. – С. 116–123.
6. Слюсар В. И. Военная связь стран НАТО: проблемы
современных технологий // Электроника: Наука, Техно-
логия, Бизнес. – 2008. – № 4. – С. 66–71.
7. Слюсар В. И. Передача данных с борта БПЛА: стандар-
ты НАТО // Электроника: наука, технология, бизнес. –
2010. – № 3. – С. 80–86.
8. Слюсар В. И. Радиолинии связи с БПЛА: примеры реа-
лизации // Электроника: наука, технология, бизнес. –
2010. – № 5. – C. 56–60.
9. Літаючі самоорганізуючі радіомережі / [В. А. Романюк,
Є. О. Степаненко, І. В. Панченко, О. І. Восколович] //
Збірник наукових праць ВІТІ. – 2017. – № 1. – С. 104–
114.
10. Романюк В. А. Задачі синтезу топологій мереж мобіль-
ної компоненти з використанням телекомунікаційних
аероплатформ / В. А. Романюк, Є. О. Степаненко // Збі-
рник наукових праць ВІТІ. – 2017. – № 3. – С. 149–157.

11. Suyi L. Machine health monitoring and prognostication via
vibration information / L. Suyi, S. Wang // Intelligent
systems design and applications : Sixth international
conference, Jinan, 16–18 October 2006 : proceedings. – Los
Alamitos: IEEE, 2006. – P. 879.
12. Субботін С. О. Програмні засоби синтезу діагностичних
і розпізнавальних моделей за прецедентами / С. О. Суб-
ботін // Сучасні проблеми і досягнення в галузі радіоте-
хніки, телекомунікацій та інформаційних технологій :
VI Міжнародна науково-практична конференція, Запо-
ріжжя, 19–21 вересня 2012 р. : тези доповідей. – Запо–
ріжжя : ЗНТУ, 2012. – С. 21–22.
13. Neagu C.-D. Using artificial neural networks in fuzzy
reasoning : abstract of the dissertation ... doctor of
philosophy in computer science / C.-D. Neagu. – Galati :
University of Galati, 2000. – 42 p.
14. Снитюк В. Є. Еволюційні технології прийняття рішень в
умовах невизначеності : автореф. дис. ... д-ра техн. наук
: 05.13.06 / НАН України; Інститут проблем математич-
них машин і систем. – К., 2009. – 36 с.
15. Li S. Automated tool condition monitoring in machining
using fuzzy neural networks : thesis ... doctor of philosophy
/ Li Shengmu. – Hamilton : McMaster University, 1995. –
187 p.
16. Воронкин Р. А. Математическое моделирование процес-
сов генетического поиска для повышения качества обу-
чения нейронных сетей прямого распространения : дис.
… канд. техн. наук : 05.13.18 / Воронкин Роман Алекса-
ндрович. – Ставрополь, 2004. – 237 с.
17. Abraham A. Engineering evolutionary intelligent systems /
A. Abraham, C. Grosan, W. Pedrycz. – Berlin : Springer,
2008. – 444 p.
18. Прогрессивные технологии моделирования, оптимиза-
ции и интеллектуальной автоматизации этапов жизнен-
ного цикла авиационных двигателей : монография /
[А. В. Богуслаев, Ал. А. Олейник, Ан. А. Олейник,
Д. В. Павленко, С. А. Субботин] ; под ред. Д. В. Павлен-
ко, С. А. Субботина. – Запорожье: ОАО “Мотор Сич”,
2009. – 468 с.
19. Машиностроение : энциклопедия / [pед. совет:
К. В. Фролов (пред.) и др.]. – М. : Машиностроение. –
Т. III-7 : Измерения, контроль, испытания и диагностика
/ [В. В. Клюев, Ф. Р. Соснин, В. Н. Филинов и др.] ; под
общ. ред. В. В. Клюева, 1996. – 464 с.
20. UCI machine learning repository [Electronic resource]. –
Access mode: http://archive.ics.uci.edu/ml/datasets/.
21. Шувакин Ю. А. Моделирование кинематики и динамики
полета беспилотного летательного аппарата / Ю. А. Шу-
вакин // Проблемы современной науки и образования:
Олимп. Иваново – 2016. № 16(58). – С. 44–47.
22. Лебедев А. А. Динамика полета беспилотных летатель-
ных аппаратов : учебное пособие для ВУЗов /
А. А. Лебедев, Л. С. Чернобровкин; под ред.
В. К. Сальника. – М., 2010. – 618 с.
23. Чернодуб А. Н. Обучение нейроэмуляторов с использо-
ванием псевдорегуляризации для метода нейроуправле-
ния с эталонной моделью / А. Н. Чернодуб // Интеллек-
туальные системы. – 2012. – № 4. – С. 602–614.
24. Mu C. Neural-network-based adaptive guaranteed cost
control of nonlinear dynamical systems with matched
uncertainties / C. Mu, D. Wang // Neurocomputing. –
2017. – Vol. 245. – P. 46–54.
25. Lin Z. Relative ordering learning in spiking neural network
for pattern recognition / Z. Lin, D. Ma, J. Meng, L. Chen //
Neurocomputing. – 2018. – vol. 275. – P. 94–106.
26. Yu J. Machine learning and signal processing for big
multimedia analysis / J. Yu, J. Sang, X. Gao //
Neurocomputing. – 2017. – Vol. 257. – P. 1–4.
27. Online adaptive optimal control for continuous-time
nonlinear systems with completely unknown dynamics /
[Y. Lv, J. Na, Q. Yang et al.] // International Journal of
Control. – 2016. – Vol. 89. – P. 99–112.
28. Automatically Designing CNN Architectures Using Genetic
Algorithm for Image Classification / [Y. Sun, B. Xue, M.
Zhang, G. G. Yen] // Cornell University Libreri. – Electronic
data. – 2018. – Mode of access:
https://arxiv.org/abs/1808.03818 (viewed on Aug 11,
2018). – Title from the screen.
29. Zela A. Towards Automated Deep Learning: Efficient Joint
Neural Architecture and Hyperparameter Search / A. Zela,
A. Klein. S. Falkner. F. Hutter // Cornell University Libreri.
– Electronic data. – 2018. – Mode of access:
https://arxiv.org/abs/1807.06906 (viewed on Jul 18, 2018). –
Title from the screen.







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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