Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Lateral Control of High Speed Flight Based on Type-2 Fuzzy Logic

Type-2 Fuzzy logic에 기반 한 고속 항공기의 횡 운동 제어

  • Song, Jin-Hwan (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Jeon, Hong-Tae (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 송진환 (중앙대학교 공과대학 전자전기공학부) ;
  • 전홍태 (중앙대학교 공과대학 전자전기공학부)
  • Received : 2013.09.23
  • Accepted : 2013.10.14
  • Published : 2013.10.25
Download PDF
⟨ Previous Next ⟩

Abstract

There exist two major difficulties in developing flight control system: nonlinear dynamic characteristics and time-varying properties of parameters of aircraft. Instead of the difficulties, many high reliable and efficient control methodologies have been developed. But, most of the developed control systems are based on the exact mathematical modelling of aircraft and, in the absence of such a model, it is very difficult to derive performance, robustness and nominal stability. From these aspects, recently, some approaches to utilizing the intelligent control theories such as fuzzy logic control, neural network and genetic algorithm have appeared. In this paper, one advanced intelligent lateral control system of a high speed fight has been developed utilizing type-2 fuzzy logic, which can deduce the uncertainty problem of the conventional fuzzy logic. The results will be verified through computer simulation.

항공기의 제어 시스템 설계에 있어 두 가지 어려움이 있다. 즉 항공기의 동적 특성이 비선형 특성을 갖고 있고 그 파라미터 값들이 시간 혹은 비행 조건에 따라 변화하는 시변 특성을 갖고 있다는 점이다. 그럼에도 불구하고 고전적인 제어 이론을 활용한 신뢰성 높고 효율적인 제어 기법들이 계속 개발되어 왔으나 정확한 이론적 분석이 수반되지 않으면 항공기의 성능, 강건성, 그리고 안전성조차도 확보하기 어려운 문제점을 갖는다. 이에 최근에는 퍼지 논리, 신경망, 유전자 알고리즘으로 대표되는 지능 제어 기법을 활용한 항공기 제어 시스템 개발이 시도 되고 있다. 본 논문에서는 기존의 퍼지 논리가 갖고 있는 불확실성에 대한 취약점들을 크게 감소시킬 수 있는 Interval Type-2 퍼지 논리 이론을 기반으로 고속 항공기의 지능형 비행 횡 제어 시스템을 개발하고 컴퓨터 모의실험에 의해 그 효용성을 입증한다.

Keywords

References

  1. J.Shamma, Michal,"Gain sceduling: potential hazards and possible remedies," Control Engineering Pract., pp. 11-21, 2001.
  2. A. Calise, R.T. Rysdyk,"Nonlinear adaptive flight control using neural networks," IEEE Control Syst. Mag., pp. 14-25, 1998.
  3. W. C. Reigelsperger, S. S. Banda,"Nonlinear simulation of a modified F-16 with full-envelope control laws," Control Eng. Pract., pp. 309-320, 1998.
  4. T. S. No, B. M. Min,"Control and simulation of arbitrary flight trajectory-tracking," trajectory-tracking,"Control Eng. Pract., pp. pp.601-612, 2005.
  5. T. Kevyczky, G.J. Balas,"Receding horizon control of an F-16 aircraft,"Control Eng. aircraft,"Control Eng. Pract., pp. 1023-1033, 2000.
  6. D. Bates, M. Hastrom,"Nonlinear analysis and synthesis techniques for aircraft control," Spring lecture notes in control and information sciences, vol. 365, 2007.
  7. E. H. K. Wong, Y. K. Wong, Y. C. Li,"Multiple specification controller design for F-16 fighters," J . Aircr. Eng. Aerosp. Technol. Technol., vol. 890, no.5, pp. 510-515, 2008.
  8. P. Melin, O. C. Castillo, "Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks and fractal theory ,"applied soft computing, vol.3, pp 353-362, 2003. https://doi.org/10.1016/j.asoc.2003.05.006
  9. M. R. Khosravan," Application of neural network on flight ontrol," Int.J ournal of Machine Learning and Computing, vol. 2, no. 6, 2012.
  10. P. Stewart, D. Gladwin, M. Parr, J. Stewart,"Multi-objective evolutionary-fuzzy augmented flight control for an F16 aircraft," Proc. IMeche, vol. 224, pp. 293-307, 2009.
  11. I. C. Choe, S. H. Ha, S. J. Kim, H. T. Jeon,"A Study on Intelligent Path Searching and Guide using RFID and Fuzzy Logic," J . of KIIS, vol. 19, no. 1, pp. 139-144, 2009. https://doi.org/10.5391/JKIIS.2009.19.1.139
  12. S. H. Ha, I. C. Choe, H. S. Kim, H. T. Jeon,"Collision Avoidance and Optimal Path planning of Autonomous Mobile Robot Using Soft-Computing," J . of KIIS, vol. 20, no. 2, pp. 195-201, 2010. https://doi.org/10.5391/JKIIS.2010.20.2.195
  13. L. A. Zadeh, "Fuzzy sets," Information and Control , vol. 8, pp. 338-353, 1965 https://doi.org/10.1016/S0019-9958(65)90241-X
  14. J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, 2001.
  15. Oscar Castillo, Patricia Melin, Type-2 Fuzzy Logic: Theory and Applications, Springer-Verlag, 2008.
  16. A. Suchkov, Aircraft Modelling Tool for the FAA Target Generation Facility: User manual , W. J. Hughes Technical report, 2001.