Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Turbojet Engine Control of UAV using Artificial Neural Network PID

인공신경망 PID를 이용한 무인항공기 터보제트 엔진 제어

  • 김대기 (한서대학교 항공전자공학과) ;
  • 홍교영 (한서대학교 항공전자공학과) ;
  • 안동만 (한서대학교 항공전자공학과) ;
  • 홍승범 (한서대학교 항공전자공학과) ;
  • 지민석 (한서대학교 항공전자공학과)
  • Received : 2014.02.23
  • Accepted : 2014.04.15
  • Published : 2014.04.30
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Abstract

In this paper, controller Propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Artificial Neural Network Error Back Propagation Algorithm. To prevent any surge or a flame out event during the engine acceleration or deceleration, the ANN PID controller effectively controls the fuel flow input of the control system. ANN PID results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine and the controller is designed to converge to the desired speed quickly and safely. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

본 논문에서는 무인항공기용 소형 터보제트엔진에 대해 압축기 서지현상 및 화염소실을 방지하면서 과도응답 특성을 개선하는 제어기를 설계하였다. 인공신경망과 PID 제어 알고리즘을 적용하는 터보제트엔진 제어기를 설계하고 인공신경망 역전파 알고리즘을 사용하였다. 터보제트 엔진의 가 감속 시 서지현상과 flame-out 현상을 방지하기 위해 연료 유량 제어 입력을 인공신경망 PID 제어기로 생성한다. 생성된 연료 유량 제어 입력은 신속하고 안전하게 원하는 속도로 수렴할 수 있도록 제어기를 설계한다. MATLAB을 이용한 시뮬레이션을 통해 이득 값에 따른 응답특성 비교 분석 및 신속하고 안전하게 원하는 속도로 수렴하는 제어성능을 확인하였다.

Keywords

References

  1. M. Montazeri-Gh, H. Yousefpour and S. Jafari, "Fuzzy logic computing for design of gas turbine engine fuel control system," in Computer and Automation Engineering (ICCAE), Singapore, Vol.5, pp. 723-727, 2010.
  2. H. Y. Cao and F. M. Peng, "Optimization of engine speed neural network PID controller based on genetic algorithm," in Computational Intelligence and Design(ISCID), Hangzhou: China, Vol.2, pp. 271-274, 2011.
  3. M. Jing, "Adaptive control of the aircraft turbojet engine based on the neural network," in International Conference. Computational Intelligence and Security, Guangzhou: China, Vol. 1, pp. 937-940, 2006.
  4. H. Badihi, A. Shahriari and A. Naghsh, "Artificial neural network application to fuel flow function for demanded jet engine performance," in IEEE Aerospace Conference, Big Sky: MT, pp 1-7. 2009.
  5. X. Yao, "Evolving artificial neural networks," Proceedings of The IEEE, Vol 87, No.9, pp. 1423-1447, Sep. 1999.
  6. I. S. Oh, Pattern Recognition, Seoul, Korea: Kyobobook, 2012.
  7. L. Xu, "General fuzzy neural network: theory and applications," in IEEE International Conference on Fuzzy Systems, Seoul: Korea, Vol. 3, pp. 1649-1654, 1999.
  8. J. Y. Lee and J. S. Lim "Artificial neural network approach to selection of enhanced oil recovery method," The Korean Society of Mineral and Energy Resources Engineers, Vol. 45, No. 6, pp. 719-726, 2008.