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A minimum selection strategy for control of small-scale turbojet engines

Published online by Cambridge University Press:  22 December 2025

A. Imani Open the ORCID record for A. Imani [Opens in a new window]
A. Imani*
Affiliation:
Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
*
Email: a.imani@basu.ac.ir
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Abstract

Control of small turbojet engines is challenging due to the small number of sensors and actuators. In these engines, typically the spool speed and exhaust gas temperature are the measured variables and the fuel flow is the only manipulated variable. However, the thrust command must be achieved and the engine’s structural and operational limitations must be safeguarded. In this research, a minimum selector control structure with a saturation function is presented for controlling small turbojet engines. One control loop is considered to control the spool speed and another loop is used to manage the exhaust gas temperature. The output of the control loops is the fuel flow rate and the minimum value is selected between them. To prevent the compressor surge and combustor blow-out during engine acceleration/deceleration, a fuel flow rate saturation is defined. Due to the switching structure of the proposed controller and existence of the saturation function, stability analysis is a critical issue. Therefore, a methodology is presented to analyse the stability of the proposed structure. In simulation study, a nonlinear thermodynamic model that matches more than 90% with the test data is used and the response of the proposed controller is compared with a proportional integral (PI) controller. In a comprehensive scenario, the throttle degree varies from 75% to 100%. Using the PI controller, some outputs have some overshoot and the exhaust gas temperature exceeds the corresponding constraint by 40K. While the proposed minimum selector controller, in addition to accurately fulfilling the thrust command, fully protects the limitations governing engine variables.

Keywords

minimum selectionsaturation functionsmall turbojet enginestability assurancethermodynamic model

Information

Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 17
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Catana, R.M., Cican, G. and Badea, G.P. Thermodynamic analysis and performance evaluation of microjet engines in gas turbine education, J. Appl. Sci., 2024, 14, p 6754, doi: 10.3390/app14156754CrossRefGoogle Scholar
Erario, M.L., De Giorgi, M.G. and Przysowa, R. Model-based dynamic performance simulation of a microturbine using flight test data, Aerospace, 2022, 9, (2), p 60, doi: 10.3390/aerospace9020060CrossRefGoogle Scholar
Gaitanis, A., Contino, F. and Paepe, W.D. Real time micro gas turbines performance assessment tool: comprehensive transient behavior prediction with computationally effective techniques, J. Eng. Gas. Turbines Power, 2023, 145, (3), p 031006, doi: 10.1115/1.4055785CrossRefGoogle Scholar
Many, M., Weber, M., Oberndorfer, S., Koeberle, S.J., Hosseini, B. and Hornung, M. System identification of a turbojet engine using multi-sine inputs in ground testing, Deutscher Luft- und Raumfahrtkongress, 2012. doi: 10.25967/550281CrossRefGoogle Scholar
Momin, A.J.A., Nava, G., L’Erario, G., Mohamed, H.A.O., Bergonti, F., Vanteddu, P.R., Braghin, F. and Pucci, D. Nonlinear model identification and observer design for thrust estimation of small-scale turbojet engines, 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, doi: 10.1109/ICRA46639.2022.9812283CrossRefGoogle Scholar
Metin, E.Y. and Aygün, H. Energy and power aspects of an experimental target drone engine at non-linear controller loads, Energy, 2019, 185, pp 981–993. doi: 10.1016/j.energy.2019.07.103CrossRefGoogle Scholar
Aygun, H., Dursun, O.O., Dönmez, K., Sahin, O. and Toraman, S. Prediction of performance characteristics of an experimental micro turbojet engine using machine learning approaches, Energy, 2024, 313, p 133997. doi: 10.1016/j.energy.2024.133997CrossRefGoogle Scholar
Banazadeh, A., Gol, H.A. and Ramazani, H. PID controller design for micro gas turbines using experimental frequency-response data and a linear identification technique, Int. J. Adv. Mechatron. Syst., 2013, 5, (6), pp 353–364, doi: 10.1504/IJAMECHS.2013.060016CrossRefGoogle Scholar
Andoga, R., Főző, L., Judičák, J., Bréda, R., Szabo, S., Rozenberg, R. and Džunda, M. Intelligent situational control of small turbojet engines, Int. J. Aerospace Eng., 2018, doi: 10.1155/2018/8328792CrossRefGoogle Scholar
Beneda, K. and Derbel, K. Design of two-degree-of-freedom turbofan-power-ratio control system for micro turbojet engine, 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herlany, Slovakia, doi: 10.1109/SAMI.2019.8782788CrossRefGoogle Scholar
Derbel, K. and Beneda, K. Sliding mode control for micro turbojet engine using turbofan power ratio as control law, Energies, 2020, 13, (18), p 4841, doi: 10.3390/en13184841CrossRefGoogle Scholar
Tang, W., Wang, L., Gu, J. and Gu, Y. Single neural adaptive PID control for small UAV micro-turbojet engine, Sensors, 2020, 20, (2), p 345, doi: 10.3390/s20020345CrossRefGoogle ScholarPubMed
Shehata, A.M., Khalil, M.K. and Ashry, M.M. Controller design for micro turbojet engine, 2020 12th International Conference on Electrical Engineering (ICEENG), pp 436–440, doi: 10.1109/ICEENG45378.2020.9171762CrossRefGoogle Scholar
Shehata, A.M., Khalil, M.K. and Ashry, M.M. Adaptive fuzzy PID controller applied to micro turbojet engine, J. Phys.: Conf. Ser., 2021, 2128, (1), p 012030, doi: 10.1088/1742-6596/2128/1/012030Google Scholar
Gao, Q., Li, J. and Zhang, Y. Self-tuning speed and flow control of micro turbojet engines based on an improved evolutionary strategy, Automatika, 2024, 65, (3), pp 1154–1162, doi: 10.1080/00051144.2024.2349869CrossRefGoogle Scholar
Wang, N., Xu, J. and Li, Z. The active disturbance rejection control method for small-scale turbojet engine, J. Phys. Conf. Ser., 2024, 2764, p 012065, doi: 10.1088/1742-6596/2764/1/012065CrossRefGoogle Scholar
Jafari, S. and Nikolaidis, T. Turbojet engine industrial Min–Max controller performance improvement using fuzzy norms, Electronics, 2018, 7, (314), doi: 10.3390/electronics7110314CrossRefGoogle Scholar
Garg, S. Aircraft Turbine Engine Control Research at NASA Glenn Research Center, NASA/TM-2013-217821, Glenn Research Center, 2013, Cleveland, Ohio.Google Scholar
Imani, A. and Montazeri-Gh, M. Improvement of min-max limit protection in aircraft engine control: an lmi approach, J. Aerospace Sci. Technol., 2017, 68, pp 214–222, doi: 10.1016/j.ast.2017.05.017CrossRefGoogle Scholar
Authié, P. Multi-model structured h-infinity design of a robust and aggressive turbofan jet engine controller, Lett. Dyn. Syst. Control, 2022, 2, (2), p 021008, doi: 10.1115/1.4052916CrossRefGoogle Scholar
Richter, H. A multi-regulator sliding mode control strategy for output constrained systems, Automatica, 2011, 47, (10), pp 2251–2259, doi: 10.1016/j.automatica.2011.08.003CrossRefGoogle Scholar
Imani, A. and Montazeri-Gh, M. A Min–Max multiregulator system with stability analysis for aeroengine propulsion control, ISA Trans., 2019, 85, pp 84–96, doi: 10.1016/j.isatra.2018.10.035CrossRefGoogle ScholarPubMed
AMT. Titan gas turbine manual, AMT Netherlands, 2014.Google Scholar
Savvaris, A., Buonanno, A., Jamil, R. and Tsourdos, A. Design and development of the DEMON UAV fluidic flight control system, 2013 In AIAA Infotech@ Aerospace (I@ A) Conference, p 4820. doi: 10.2514/6.2013-4820CrossRefGoogle Scholar
Walsh, P.P. and Fletcher, P. Gas Turbine Performance, John Wiley & Sons, 2004, doi: 10.1002/9780470774533CrossRefGoogle Scholar
Lichtsinder, M. and Levy, Y. Jet engine model for control and real-time simulations, J. Eng. Gas Turb. Power, 2006, 128, (4), pp 745–753, doi: 10.1115/1.1915391CrossRefGoogle Scholar
Zinnecker, A., Chapman, J.W., Lavelle, T.M. and Litt, J.S. 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, Cleveland, OH, 2014, doi: 10.2514/6.2014-3930CrossRefGoogle Scholar
Chung, G.Y., Prasad, J., Dhingra, M. and Meisner, R. Real time analytical linearization of turbofan engine model, J. Eng. Gas Turb. Power, 2014, 136, (1), p 011201, doi: 10.1115/1.4025310CrossRefGoogle Scholar
Cai, C., Wang, Y., Chen, H., Zheng, Q. and Zhang, H. Full-envelope acceleration control method of turbofan engine based on variable geometry compound adjustment, J. Aerospace Sci. Technol., 2022, 128, p 107748, doi: 10.1016/j.ast.2022.107748CrossRefGoogle Scholar
Eslami, M. and Banazadeh, A., Control performance enhancement of gas turbines in the minimum command selection strategy, ISA Trans., 2021, 112, pp 186–198, doi: 10.1016/j.isatra.2020.12.003CrossRefGoogle ScholarPubMed
Singh, R., Maity, A. and Nataraj, P.S.V. Dynamic modeling and robust nonlinear control of a laboratory gas turbine engine, J. Aerospace Sci. Technol., 2022, 126, p 107586, doi: 10.1016/j.ast.2022.107586CrossRefGoogle Scholar
DeCastro, J.A., Litt, J.S. and Frederick, D.K. A modular aero-propulsion system simulation of a large commercial aircraft engine, NASA/TM2008-215303, Glenn Research Center, Cleveland, Ohio, 2008.10.2514/6.2008-4579CrossRefGoogle Scholar
Litt, J.S., Frederick, D.K. and Guo, T.H. The case for intelligent propulsion control for fast engine response, NASA/TM2009-215668, Glenn Research Center, Cleveland, Ohio, 2009.10.2514/6.2009-1876CrossRefGoogle Scholar
Khalil, H.K. Nonlinear Control, Global Ed, Pearson Education, 2015.Google Scholar
Imani, A. and Montazeri-Gh, M. A Multi-loop switching controller for aircraft gas turbine engine with stability proof, Int. J. Control Autom., 2019, 17, (6), pp 1359–1368, doi: 10.1007/s12555-018-0803-5CrossRefGoogle Scholar
Haddad, W.M. and Bernstien, D.S. Explicit construction of quadratic lyapunov functions for the small gain, positivity, circle, and popov theorems and their application to robust stability. Part I: continuous-time theory, Int. J. Robust Nonlinear, 1993, 3, (4), pp 313–339, doi: 10.1002/rnc.4590030403CrossRefGoogle Scholar
Geromel, J.C. Differential Linear Matrix Inequalities: In Sampled-Data Systems Filtering and Control, Springer, 2023, doi: 10.1007/978-3-031-29754-0CrossRefGoogle Scholar