Scholarly record
APPLICATION OF NEURAL FUZZY CONTROL TO ADAPT THE PHYSICAL LAYER OF A FLYING SENSOR NETWORK
Abstract
Modern unmanned aerial vehicles (UAVs) are used to perform a wide range of flight tasks (military and civil), implemented both in autonomous flight mode and by remote control. One of the advanced technologies for their use is the organization of flying sensor networks (FSN). The quality of information exchange in FSN, widely used for monitoring of various natural and technical objects, depends significantly on the conditions of signal propagation and the level of external interference. A promising method for improving FSN performance is to adapt the transmission mode depending on the signal-to-noise ratio at the input of UAV receivers, namely, changing the encoding speed and modulation method. To adapt the physical level of the flying sensor network, it is proposed to use a hybrid method of network management based on artificial neural networks (ANN) and fuzzy logic. For practical implementation of the proposed approach, a functional scheme of a closed automatic control system with negative feedback for the receiving and transmitting module of UAV radio transmitters is proposed. The scheme is based on a fuzzy controller with an auto-tuning unit based on an artificial neural network with a single hidden layer. This functional scheme of the automatic control system of the physical layer of the OSI network model for the receiving and transmitting module of UAV radio transmitters can be built on the basis of all possible known variants of controllers (proportional P, integral I, proportionalintegral PI, differential D, PID, etc.). The fuzzy controller is synthesized using the Takagi-Sugeno fuzzy inference algorithm.
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Chertova O.G., Chirov D.S. Building a core communication network which is based on small size unmanned aircraft vehicle without ground infrastructure. H&ES Research. 2019. Vol. 11. No. 3. Pр. 60–71.
Bondarev A.N., Kirichek R.V. Overview of public-use unmanned aerial vehicles and air traffic control UAVs in different countries / Information technologies and telecommunications. - 2016, Volume 4, no. 4. - Pp. 13-23.
Leonov A.V., Chaplygin V.A. FANET / Omsk scientific bulletin- 2015, no. 3 (143). - Pp. 297-301.
Bekmezci I., Sahingoz O.K., Temel S., Flying Ad-Hoc Networks (FANETs): A Survey, Ad Hoc Networks, 2013, Vol. 11, № 3, pp. 1254–1270.
Ananyev A.V., Stafeev M.A., Makeev E.V. Development of a method for organizing communications using unmanned aerial vehicles of short range / Proceedings of the MAI. Issue # 105. - 2019. - Pp. 1-18.
Polynkin A.V., Le H.T. Research of characteristics of the radio communication channel with unmanned aerial vehicles / Izvestiya Tulsu. Technical science. - 2013, Vol. 7., Part 2. - Pp. 98-107.
Fokin G.A. Review of models of the radio channel of communication with unmanned aerial vehicles / / proceedings of educational institutions of communication. 2018. Vol. 4. # 4. Pp. 85-101.
Kulikov G.V., Tambovsky S.S. Evaluation of the quality of communication with UAVs in urban development / Vestnik MSTU MIREA. - 2015, no. 1 (6). - Pp. 205-217.
Vasilyev G.S., Surzhik D.I., Kuzichkin O.R., Kurilov I.A. "Algorithms for Adapting Communication Protocols of FANET Networks," Journal of Software vol. 15, no. 4, pp. 114-122, 2020. ISSN: 1796-217X DOI: 10.17706/jsw.15.4.114-122.
Konstantinov I.S., Vasilyev G.S., Kuzichkin O.R., Surzhik D.I., Lazarev S.A. Numerical and Analytical Modeling of Wireless UV Communication Channels for the Organization of Wireless Ad-Hoc Network // IJCSNS - International Journal of Computer Science and Network Security - 2018. - Vol. 18, No. 8, pp. 98-104.
Zadeh L.A. Fuzzy sets. - Information and Control. 1965, №8, p.338-353.
Mamdani E.H. Application of fuzzy algorithms for control of simple dynamic plant. - Proc. Inst. Elect. Eng. Contr. Sci., vol. 121, 1974, p. 1585–1588.
Yesil E.; Guzelkaya M.; Eksin I. Internal model control based fuzzy gain scheduling technique of pid controllers. - World Automation Congress, 2004. Proceedings. Vol. 17, 28 June - 1 July 2004, p.501 - 506.
Hsuan M.F. A self-tuning fuzzy control system design. - IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th. Vol. 1, 25-28 July 2001, p. 209 - 214 vol.1.
Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. - Int. J. Man-Mach. Stud., vol. 7, 1975, p. 1–13.
Mann G.K., Bao-Gang Hu., Gosine R.G. Analysis of direct action fuzzy PID controller structures. - IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 29, Issue 3, Jun 1999, p. 371 - 388.
Terekhov V.A., Efimov D.V., Tyukin I.Y. Neural network control systems. Moscow: Higher school, 2002. - 184 p. - ISBN 5-06-004094-1.
Kawafuku R., Sasaki M., Kato S. Self-tuning PID control of a flexible micro-actuator using neural networks. - Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on. Vol. 3, 11-14 Oct 1998, p. 3067 - 3072 vol.3.
Kato M., Yamamoto T., Fujisawa S. A. Skill-Based PID Controller Using Artificial Neural Networks. - Computational Intelligence for Modeling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 1, 28-30 Nov. 2005, p. 702 – 707.
Astrom K.J., Hagglund T. Advanced PID control. - ISA - The Instrumentation, Systems, and Automation Society, 2006, 460 p.
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