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Decoupled controller for mixed exhausts turbofan engine

Published online by Cambridge University Press:  03 February 2016

T. R. Nada
T. R. Nada*
Affiliation:
National Authority of Remote Sensing and Space Sciences, Cairo, Egypt
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Abstract

This paper points out the capabilities of fully decoupled fuzzy controller which introduces simple design approach to deal with the coupling effects in controlling two spools, mixed exhausts turbofan engines. The decoupling is performed through proper selection of input parameters to the controller. Digital nonlinear engine/control system simulation is used to construct the fuzzy rules depending on simple logic. The performance of this controller is compared with that of an optimal controller representing efficient classical and conventional techniques. The decoupled fuzzy control system produces favorable transient strategies that other conventional controllers can not attempt due to its inherent proportionality characteristics. It displays improvements in surge margin for both fan and compressor, and temperature margin with almost similar response time during acceleration. Also, the proposed controller has the capabilities to increase the response speed during deceleration independently from acceleration transient.

Type
Research Article
Information
The Aeronautical Journal , Volume 110 , Issue 1114 , December 2006 , pp. 783 - 792
Copyright
Copyright © Royal Aeronautical Society 2006 

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References

1. Monopoli, R.V., Model following control of gas turbine engines, ASME J of Dynamic Systems, Measurement, and Control, September 1981, 103, pp 285–289.Google Scholar
2. Polley, J.A., Adibhatala, S. and Hoffman, P.J., Multivariable turbofan engine control for full flight envelope operation, ASME, J of Engineering for Gas Turbine and Power, January 1989, 111, pp 130–137.Google Scholar
3. Garassino, A., An advanced control system for turbofan engine: multivariable control and fuzzy logic, Application to the M88-2 Engine, The International Gas Turbine and Aero-engine Congress and Exposition, 5–8 June 1995, Houston, Texas, USA, ASME 95-GT-344.Google Scholar
4. Turso, J.A. and Litt, J.S., Intelligent, robust control of deteriorated turbofan engines via linear parameter varying quadratic lyapunov function design, November 2004, NASA TM-2004-213375.Google Scholar
5. Zilouchian, A. et al Design of a fuzzy logic controller for a jet engine fuel system, Control Engineering Practice, 2000, 8, pp 873–883.Google Scholar
6. Chi-Hua, W., Yun-Hua, X. and Ben-Wei, L., Application of a fuzzy controller in the fuel system of a turbojet engine, J Propulsion and Power, May-June 1989, 5, (3).Google Scholar
7. Wu, C.-H. and Luo, E.-K., Model reference adaptive control system on an aeroengine, J Propulsion and Power, 1995, 11, (2), pp 371– 374.Google Scholar
8. Zhongxiang, F. and Wu, Chihua, Application of two variable fuzzy-PI control in an aero engine, AIAA, J of Propulsion and Power, 14, (3), May-June 1998.Google Scholar
9. Chipperfield, A.J., Bica, B. and Fleming, P.J., Fuzzy scheduling control of a gas turbine aero-engine: A multiobjective approach, IEEE Transactions on industrial electronics, June 2002, 49, (3), pp 536–548.Google Scholar
10. General Electric, F110-GE-100 Pilot Awareness Program, User’s Manual, 1989.Google Scholar
11. Stimler, D.M., Scheduling turbofan engine control set points by semiinfinite optimization, IEEE, American Control Conference, 7th, Atlanta GA, USA, June 1998, Proceedings, 3, pp 2256–2263.Google Scholar
12. Sanghi, V., Lakshmanan, B.K. and Sundararajan, V., Digital simulator for steady state performance prediction of military turbofan engine, AIAA, J of Propulsion and Power, January-February 1998, 14, (1).Google Scholar
13. Daniele, C.J., Krosel, S.M. and Szuch, J.R., Digital computer program for generating dynamic turbofan engine models (DIGTEM), NASA TM-83446, September 1983.Google Scholar
14. Schobeiri, T., Attia, M. and Lippke, C., Getran: A generic, modularly structured computer code for simulation of dynamic behavior of aero-and power generation gas turbine engines, J of Engineering for Gas Turbines and Power, July 1994, 116, pp 483–494.Google Scholar
15. Mu, J., Rees, D. and Chiras, N., Optimum gain-scheduling PID controllers for gas turbine engines based on NARMAX and neural network models, Proceedings of ASME Turbo Expo 2003, Power for Land, Sea, and Air, 16-19 June, 2003, USA.Google Scholar
16. Procyk, T.J. and Mamdani, E.H., A Linguistic self-organizing process controller, J Astronautica, 1979, 15, pp 15–30.Google Scholar
17. Jang, J.R. and Sun, C., Neuro-fuzzy modelling and control, Proceedings of the IEEE, 83, (3), March 1995.Google Scholar