Measuring model of helicopter’s hovering stabilization parameters against point objects

Authors

  • V. M. Kazak, National Aviation University
  • D. O. Shevchuk National Aviation University
  • N. A. Tymoshenko National Aviation University
  • I. V. Prochorenko National Aviation University

DOI:

https://doi.org/10.18372/1990-5548.49.11249

Keywords:

Control, control system, helicopter, filter, perturbation, stabilization

Abstract

Here we explain measuring model of helicopter's hovering stabilization parameters againstpoint object that requires smaller volume of calculation and preserves high speed and accuracy ofstabilization. Also we investigate reduction of number of state vectors and volume of calculation viausage of extended Kalman filter and standard sensors

Author Biographies

V. M. Kazak,, National Aviation University

Doctor of Engineering. Professor. New Technologies Center

D. O. Shevchuk, National Aviation University

Doctor of Engineering. Senior Researcher. Department of Automation and Energy Management

N. A. Tymoshenko, National Aviation University

Candidate of Engineering. Teaching Fellow. Department of Automation and Energy Management

I. V. Prochorenko, National Aviation University

Candidate of Engineering. Teaching Fellow. Department of Automation and Energy Management

References

V. Beltsov, “Holdin the Sky In the Strong Hands.” All-Russian aerospace journal Herald of Aviation and Cosmonautics, no. 1, pp. 8–10, 2000.

W. Raymond Prouty and H. C. Curtiss, "Helicopter Control Systems: A History," Journal of Guidance, Control, and Dynamics, vol. 26, no. 1, pp. 12–18, 2003.

R. W. Prouty, Helicopter Performance, Stability, and Control. first ed. Krieger Publishing Company, London, United Kingdom, 2002, 746 p.

V. M. Kazak, D. O. Shevchuk, N. A. Tymoshenko, and I. V. Prochorenko, “Method of State Estimation and Identification of the Arial Vehicle under Destabilizing Action of Weather Conditions.” IEEE 4th International Conference “Methods and Systems of Navigation and Motion Control” Conference Proceedings, 18-20 October 2016, Kyiv, Ukraine, pp. 110–116.

T. Soderstrom, P. Stoica, and B. Friedlander, “An indirect prediction error method for system

identification.” Automatica, vol. 27, no. 1, pp. 183–188, 1991.

V. M. Kazak, System recovery methods survivability of aircraft in special situations in flight: monograph. Kyiv, NAU, 2010, 284 p.

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Issue

Vol. 3 No. 49 (2016)

Section

MATHEMATICAL MODELING OF PROCESSES AND SYSTEMS

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