A Model Reference Adaptive Sliding Mode Control Method for Aero-Engine

Hongliang Xiao; Huacong Li; Kai Peng; Jia Li

doi:10.1515/tjj-2019-0041

Article

A Model Reference Adaptive Sliding Mode Control Method for Aero-Engine

Hongliang Xiao , Huacong Li , Kai Peng and Jia Li

Published/Copyright: December 25, 2019

Published by

Become an author with De Gruyter Brill

Author Information

From the journal International Journal of Turbo & Jet-Engines Volume 39 Issue 4

Abstract

In this paper, a model reference adaptive sliding mode control method is proposed for a variable cycle engine (VCE) which is a MIMO system with uncertainties and external disturbances. The reference model is designed based on optimal LQR method to provide ideal reference states. An adaptive sliding mode controller (ASMC) is designed for model reference adaptive control structure, which the adaptive law is derived based on Lyapunov function to estimate the unknown upper bound of uncertainties and external disturbances. Simulation results for VCE demonstrate the performance and fidelity of the proposed method.

Keywords: multi-variable control; model reference; adaptive sliding mode control; uncertainty

PACS: 07.05.Dz; 45.80.+ r; 87.19.lr

Acknowledgements

The authors thank the support of National Science and Technology Major Project under Grant 2017-V-0010-0060 and 2017-V-0013-0065, National Natural Science Foundation of China under Grant 51506176 and Aeronautics Power Foundation under Grant 6141B090302.

Nomenclature

x: plant state vector
u: control input vector
y: plant output vector
H: altitude
M a: mach number
n l: RPM of low pressure spool
n h: torque of fan
T 4: total temperature of combustor
w f: mass of fuel flow
A 8: area of nozzle throat
A 163: area of rear variable area bypass injector
e p r s: static engine pressure ratio
l e p r: linear engine pressure ratio
e y I: error between the system output and control command
r c m d: control command
K x T: the reference model gain matrix
A , B , C , D: the system matrices with appropriate dimensions
A a u g , B a u g _, C a u g , D a u g: the augmented matrices of system
A r e f , B r e f _, C r e f: the matrices of reference model
Δ A: perturbation matrix of A
Δ B: perturbation matrix of B
η ( t ): unknown bounded disturbance vector
s: sliding function
G: gain matrix
e: tracking error of state
Φ: upper bound of perturbation matrix A
Θ: upper bound of perturbation matrix B
Ψ: upper bound of disturbance η ( t )
ξ ( t ): switch coefficient
V: Lyapunov function
Φ ˆ: estimated value Φ
Θ ˆ: estimated value Θ
Ψ ˆ: estimated value Ψ
ASMC: adaptive sliding mode controller
SMC: sliding mode controller
VCE: variable cycle engine
MRAC: Model reference adaptive control
FVABI: front variable area bypass injector
RVABI: rear variable area bypass injector
MSV: mode select valve

References

1. Liu TJ, Du X, Sun XM, Richter H, Zhu F. Robust tracking control of aero-engine rotor speed based on switched LPV model. Aerosp Sci Technol. 2019;91:382–90. DOI:10.1016/j.ast.2019.05.031.Search in Google Scholar

2. Ruffles PC. Aero engines of the future. Aeronaut J. 2003;107:307–21.10.1017/S0001924000013610Search in Google Scholar

3. Ahmadian N, Khosravi A, Sarhadi P. Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models. Mech Syst Signal Process. 2017;92:1–12. DOI:10.1016/j.ymssp.2017.01.023.Search in Google Scholar

4. Di Massa G, Russo R, Strano S, Terzo M. System structure identification and adaptive control of a seismic isolator test rig. Mech Syst Signal Process. 2013;40:736–53. DOI:10.1016/j.ymssp.2013.06.029.Search in Google Scholar

5. Pakmehr M, Yucelen T Adaptive control of uncertain systems with gain scheduled reference models and constrained control inputs. 2014 American Control Conference. IEEE, 2014:691–6.10.1109/ACC.2014.6859326Search in Google Scholar

6. Yu J, Sideris A. H∞ Control with parametric lyapunov functions. Syst Control Lett. 1997;30:57–69. DOI:10.1016/s0167-6911(96)00075-8.Search in Google Scholar

7. Khalil HK. Nonlinear systems. Upper Saddle River: Prentice Hall, 2002.Search in Google Scholar

8. Ahmadian N, Khosravi A, Sarhadi P. A new approach to adaptive control of multi-input multi-output systems using multiple models. J Dyn Syst Meas Control. 2015;137:091009. DOI:10.1115/1.4030611.Search in Google Scholar

9. Lavretsky E, Wise KA. Robust and adaptive control. London: Springer, 2013.10.1007/978-1-4471-4396-3Search in Google Scholar

10. Gadient R. Adaptive control with aerospace applications. PHD diss. University of Southern California, 2013.Search in Google Scholar

11. Han JQ. Active disturbance rejection control technique-the technique for estimating and compensating the uncertainties. Beijing: National Defense Industry Press, 2008.Search in Google Scholar

12. Gao WB. Variable structure control theory basis. Beijing: Science and Technology of China Press, 1998.Search in Google Scholar

13. Miao ZG, Xie SS, Zhang B. Adaptive global fast non-singular Terminal sliding mode control for aero-engine. J Aerospace Power. 2013;28:2634–40.Search in Google Scholar

14. Miao ZG, Xie SS, He XR. Global sliding mode control for aeroengine based on PSO network. J Aerospace Power. 2011;32:220–224+234.Search in Google Scholar

15. Ren LT, Xie SS, Pen JB. Adaptive robust sliding mode control for aeroengine T-S distributed system. J Propul Technol. 2016;37:2366–76.Search in Google Scholar

16. Sun H, Liu SM, Deng QC. Research of optimizing sliding mode control using reaching law in aero-engine. Thermal Turbine. 2015;44:30–34+45.Search in Google Scholar

17. Sun H, Liu SM, Deng QC. Comparative research on adaptive sliding mode control in aero-engine. Gas Turbinge Technol. 2015;28:10–15+34.Search in Google Scholar

18. Richter H. Multiple sliding modes with override logic: Limit management in aircraft engine controls. J Guidance, Control, Dyn. 2012;35:1132–42. DOI:10.2514/1.55922.Search in Google Scholar

19. Richter H. A multi-regulator sliding mode control strategy for output-constrained systems. Automatica. 2011;47:2251–9. DOI:10.1016/j.automatica.2011.08.003.Search in Google Scholar

20. Richter H. Advanced control of turbofan engines. London: Springer Science & Business Media, 2011.10.1007/978-1-4614-1171-0Search in Google Scholar

21. Du X, Richter H, Guo Y. Multivariable sliding-mode strategy with output constraints for aeroengine propulsion control. J guidance, Control, and Dyn. 2016;39:1631–42. DOI:10.2514/1.g001802.Search in Google Scholar

22. Pakmehr M, Wang T, Jobredeaux R. Verifiable control system development for gas turbine engines. In: CoRR abs/1311.1885. 2013;78.Search in Google Scholar

23. Flack RD. Fundamentals of jet propulsion with applications. New York: Cambridge University Press, 2010.Search in Google Scholar

24. Kulikov GG, Thompson HA. Dynamic modelling of gas turbines. London: Springer London, Limited, 2013.Search in Google Scholar

25. Xiao HL, Li HC, Li J. Modeling method of VCE based on QPSO hybrid algorithm. J Beijing Univ Aeronaut Astronaut. 2018;44:305–15.Search in Google Scholar

26. Menon P, Sweriduk G, Vaddi S. Nonlinear control of a high-performance aircraft engine. AIAA Guidance, Navigation, and Control Conference and Exhibit, 2006:6087.10.2514/6.2006-6087Search in Google Scholar

27. Edwards C, Spurgeon SK. Sliding mode control. London: Taylor and Francis, 1998.10.1201/9781498701822Search in Google Scholar

28. Utkin VI. Sliding modes in control and optimization. Commun Control Eng. 1992;189:1372–9.10.1007/978-3-642-84379-2Search in Google Scholar

29. Utkin VI. Sliding modes in control and optimization. Berlin: Springer-Verlag, 1992.10.1007/978-3-642-84379-2Search in Google Scholar

Received: 2019-11-16

Accepted: 2019-12-04

Published Online: 2019-12-25

Published in Print: 2022-12-16

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/tjj-2019-0041

Keywords for this article

multi-variable control; model reference; adaptive sliding mode control; uncertainty