EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 20 / No 3 (2019) Mechanics & Industry, 20 3 (2019) 303 References
Open Access
Issue
Mechanics & Industry
Volume 20, Number 3, 2019
Article Number 303
Number of page(s) 18
DOI https://doi.org/10.1051/meca/2019012
Published online 29 May 2019
  1. J.D. Mattingly, L.C. Jaw, Aircraft engine controls: Design, system analysis, and health monitoring (First Ed.), AIAA, Reston, 2009 [Google Scholar]
  2. J.D. Mattingly, W.H. Heiser, D.T. Pratt, Aircraft engine design (Second Ed.), AIAA, Reston, 2002 [CrossRef] [Google Scholar]
  3. G.G. Kulikov, H.A. Thompson, Dynamic modelling of gas turbines: Identification, simulation, condition monitoring, and optimal control, Springer, London. G.G, 2004 [Google Scholar]
  4. K. Lietzau, A. Kreiner, The use of onboard real-time models for jet engine control, MTU Aero Engine, Germany, 2004 [Google Scholar]
  5. M.G. Ballin, A high fidelity real-time simulation of a small turboshaft engine, NASA TM-1009 91, 1988 [Google Scholar]
  6. J.A. DeCastro, J.S. Litt, D.K. Frederick, A modular aero-propulsion system simulation of a large commercial aircraft engine, 44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2008 [Google Scholar]
  7. S.M. Jones, An introduction to thermodynamic performance analysis of aircraft gas turbine engine cycles using the numerical propulsion system simulation code, NASA/TM-2007-214690, 2007 [Google Scholar]
  8. M. Lichtsinder, Y. Levy, Jet engine model for control and real-time simulations, ASME J. Eng. Gas Turbines Power 128 (2006) 745–753 [CrossRef] [Google Scholar]
  9. D. Frederick, S. Garg, S. Adibhatla, Turbofan engine control design using robust multivariable control technologies, IEEE Trans. Control Syst. Technol. 8 (2000) 961–970 [Google Scholar]
  10. D. Yu, X. Liu, W. Bao, Z. Xu, Multiobjective robust regulating and protecting control for aeroengines, ASME J. Eng. Gas Turbines Power 131 (2009) 27–36. [Google Scholar]
  11. M. Montazeri-Gh, E. Mohammadi, S. Jafari, Fuzzy-based gas turbine engine fuel controller design using particle swarm optimization, Appl. Mech. and Mater. 110–116 (2012) 3215–3222 [Google Scholar]
  12. J. Mu, D. Rees, G.P. Liu, Advanced controller design for aircraft gas turbine engines, Control Eng. Pract. 13 (2005) 1001–1015 [Google Scholar]
  13. H. Richter, A. Singaraju, J.S. Litt, Multiplexed predictive control of a large commercial turbofan engine, J. Propuls. Power 31 (2008) 273–281 [Google Scholar]
  14. B.J. Brunell, D.E. Viassolo, R. Prasanth, Model adaptation and nonlinear model predictive control of an aircraft engine, Proceedings of ASME Turbo Expo, power for land, sea, and air, 2004 [Google Scholar]
  15. N. Sugiyama, Derivation of system matrices from nonlinear dynamic simulation of jet engines, J. Guid. Control Dyn. 17 (1994) 1320–1326 [Google Scholar]
  16. G.Y. Chung, J.V.R. Prasad, M. Dhingra, R. Meisner, Real time analytical linearization of turbofan engine model, ASME J. Eng. Gas Turbines Power 136 (2014) 1–13 [Google Scholar]
  17. R.L. DeHoff, W.E. Hall Jr., Multivariable quadratic synthesis of an advanced turbofan engine controller, J. Guid. Control Dyn. 1 (1978) 136–142 [Google Scholar]
  18. J.A. Policy, S. Adibhatla, P.J. Hoffman, Multivariate turbofan engine control for full flight envelope operation, ASME J. Eng. Gas Turbines Power 111 (1989) 130–137 [CrossRef] [Google Scholar]
  19. A.K. Chakrabarti, B. Bandyopadhyay, Controller design for a gas turbine using periodic output feedback, ASME J. Eng. Gas Turbines Power 125 (2003) 613–616 [CrossRef] [Google Scholar]
  20. F. Lu, Y. Lv, J. Huang, X. Qiu, A model-based approach for gas turbine engine performance optimal estimation, Asian J. Control 15 (2013) 1794–1808 [Google Scholar]
  21. A.M. Zinnecker, J.W. Chapman, T.M. Lavelle, J.S. Litt, Development of a twin-spool turbofan engine simulation using the toolbox for modeling and analysis of thermodynamic systems (T-MATS), 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, 2014 [Google Scholar]
  22. A.H. Nayfeh, Perturbation methods, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2004 [Google Scholar]
  23. M.H. Holmes, Introduction to perturbation methods (Second Ed.), Springer Science + Business Media, New York, 2013 [CrossRef] [Google Scholar]
  24. M. Kaminski, The stochastic perturbation method for computational mechanics (First Ed.), John Wiley & Sons, New York, 2013 [CrossRef] [Google Scholar]
  25. D.E. Goldberg, Genetic algorithms in search, optimization and machine learning (First Ed.), Addison-Wesley, 1989 [Google Scholar]
  26. Z. Michalewicz, Genetic algorithms + data structures = evolution programs (Third Ed.), AI Series, Springer, New York, 1992 [CrossRef] [Google Scholar]
  27. C. Houck, J. Joines, M.G. Kay, A genetic algorithm for function optimization: A MATLAB implementation, NCSU-IE TR 9509, 1995 [Google Scholar]
  28. K.F. Man, K.S. Tang, S. Kwong, Genetic algorithms: Concepts and applications, IEEE Trans. Ind. Electron. 43 (1996) 519–534 [Google Scholar]
  29. J. Branke,Evolutionary optimization in dynamic environments (First Ed.), Springer Science + Business Media, New York, 2002 [CrossRef] [Google Scholar]
  30. ] L. Ljung, System identification – Theory for the user (Second Ed.), Printice Hall, New Jersey, 1999 [Google Scholar]
  31. ] K.J. Keesman, System identification – An introduction, Springer-Verlag, London, 2011 [Google Scholar]
  32. D.L. Simon, S. Garg, Optimal tuner selection for kalman filter-based aircraft engine performance estimation, ASME J. Eng. Gas Turbines Power 132 (2010) 152–161 [CrossRef] [Google Scholar]
  33. D.K. Chaturvedi, Modeling and simulation of systems using MATLAB and simulink (First ed.), CRC Press, Boca Raton, 2010 [Google Scholar]
  34. E. Mohammadi, M. Montazeri-Gh, A new approach to the gray-box identification of wiener models with the application of gas turbine engine modeling, ASME J. Eng. Gas Turbines Power 137 (2015) 11–12 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.