Issue 1 (195), article 1

DOI:https://doi.org/10.15407/kvt195.01.005

Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Gritsenko V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
e-mail: vig@irtc.org.ua

Volkov O.Ye., Acting Head of Department,
Intelligent Control Department,
e-mail: alexvolk@ukr.net

Komar M.M., Researcher,
Intelligent Control Department,
e-mail: nickkomar08@gmail.com

Shepetukha Yu.M., PhD (Engineering)
Leading Researcher,
Intelligent Control Department,
e-mail: dep185@irtc.org

Voloshenyuk D.O., Researcher,
Intelligent Control Department,
e-mail: dep185@irtc.org

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkov av., 40, Kiev, 03187, Ukraine

INTEGRAL-ADAPTIVE AUTOPILOT AS A MEANS OF INTELLECTUALIZING A MODERN UNMANNED AERIAL VEHICLE

Introduction. At present unmanned aerial vehicles (UAVs) are successfully used in various industries in performing scientific and engineering, economical, military and a number of other missions. Effectiveness of their functioning is mainly determined by an onboard suit of hardware and software of a UAV’s control system. The process of the existing autopilot systems enhancement is intended to broaden the range of UAV’s tasks without direct human involvement and introduce additional smart functions into autopilot operation.

Purpose. The aim of research is to study the modern algorithms used in autopilots of unmanned aerial vehicles and formulation of the problem of development and usage of new intellectual methods for automatic control systems.

Results. The approach considered in the article is based on the theory of high-precision remote control of dynamic objects and on the complex interaction of methods of theory of invariance, adaptive control and intellectualization of processes of UAV control.

One of the features of the proposed method of intellectual control for unmanned aerial vehicle autopilot is the procedure of transforming a multi-dimensional system into an aggregate of virtual autonomous processes, for each of which the control algorithm is easily generated by an autonomous subsystem. Coming up next is the procedure of coordination of actions of all the autonomous systems into single functioning complex. This provides an opportunity to improved precision and sustainability of control.

Conclusion. Using the method described in the article allows creating integral and adaptive autopilots to perform complicated spatial maneuvering an unmanned aerial vehicle being based on usage of full non-linear models without simplifications and linearization.

Keywords: unmanned aerial vehicle, control system, virtual control, adaptation invariance.

Download full text!

REFERENCES

  1. Fahlstrom P., Gleason T. Introduction to UAV systems. Hoboken: Wiley, 2012. 4th ed. 308 p.
  2. Moiseyev V.S. Applied theory of unmanned aerial vehicles control. Kazan: GBU Republican centre for monitoring education quality, 2013.p. 768. (in Russian).
  3. Beard R.W., McLain T.W. Small Unmanned Aircraft: Theory and Practice. Princeton: Princeton Univ. Press, 2012. 320 р.
  4. Chao H., Cao Y., and Chen Y.Q. Autopilots for small unmanned aerial vehicles: a survey. International Journal of Control, Automation, and Systems, 2010, vol. 8, № 1. P. 36–44.
  5. Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Transactions on Fuzzy Systems, 2006, vol. 14, № 5. P. 676 – 697.
  6. Shilov K.Ye. Development of unmanned air vehicle automatic control system of a rotorcraft. Works of MFTI, 2014. № 4 . P. 139–152. (in Russian).
  7. Calise A., Rysdyk R. Nonlinear Adaptive Flight Control Using Neural Networks. Control Systems Magazine, 1998, vol. 18, №. 6. P. 14–25.
  8. Johnson E.N., Kannan S.K. Adaptive Flight Control for an Autonomous Unmanned Helicopter. AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, August, 2002.
  9. Lopez J., Dormido R., Gomez J.P., Dormido S., Diaz J.M. Comparison of H-infinity with QFT applied to an Altitude Command Tracker for an UAV. Proc. of the European Control Conference (2 – 5th of July, 2007, Kos, Greece) Kos, Greece, 2007. P. 46–54.
  10. Lopez J., Dormido R., Dormido S., Gomez J.P. A Robust Controller for an UAV Flight Control System. The Scientific World Journal. 2015. vol. 2015. P. 15–26.
  11. Ross T.J. Fuzzy Logic with Engineering Applications, 2nd Edition. NY: Wiley, 2004. 228 p.
  12. Kumon M., Udo Y., Michihira H., Nagata M., Mizumoto I., Iwai Z. Autopilot system for kiteplane. IEEE/ASME Transactions on Mechatronics. 2006. vol. 11. № 5. P. 615–624.
  13. Albus J.S. On intelligence and its dimensions. Technical report of the ISIS (Interdisciplinary studies of intelligent systems) group №. ISIS 94-001. University of Notre Dame, 1994. P. 11–13.
  14. Antsaklis P.J. On autonomy and intelligence in control. Technical report of the ISIS (Interdisciplinary studies of intelligent systems) group №. ISIS 94-001. University of Notre Dame, 1994. P. 14–18.
  15. Grytsenko V.I., Volkov O.E., Komar М.М., Bogachuk Yu.P. Intellectualization of the modern automatic control systems for unmanned aerial vehicles. Kibernetika i vyčislitel`naâ tehnika. 2018. № 1 (191). P. 45–59. (in Ukrainian)
  16. Pavlov V.V., Pavlova S.V. Intellectual control of complex non-linear dynamic systems. Kiev: Naukova dumka. 2015. 216 p. (in Russian).
  17. Kharchenko V.P., Chepizhenko V.I., Tounik A.A., Pavlova S.V. Unmanned aerial vehicles avionics. Kiev: TOV Abris-print, 2012. 464 p. (in Ukrainian).
  18. Bodner V.A. Air vehicle control systems. Мoscow: Mashinostroyeniye, 1973. 501 p.
    (in Russian).

Received 19.11.2018