Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Metaheuristic Design and Optimization of Fuzzy-Based Gas Turbine Engine Fuel Controller Using Hybrid Invasive Weed Optimization/Particle Swarm Optimization Algorithm

[+] Author and Article Information
E. Mohammadi

e-mail: ehs_mohammadi@iust.ac.ir

M. Montazeri-Gh

e-mail: montazeri@iust.ac.ir

P. Khalaf

e-mail: P_Khalaf@mecheng.iust.ac.ir
Systems Simulation and Control Laboratory,
Department of Mechanical Engineering,
Iran University of Science and Technology (IUST),
Tehran 16846-13114, Iran

1Corresponding author.

Contributed by the Controls, Diagnostics and Instrumentation Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received April 3, 2013; final manuscript received October 20, 2013; published online November 27, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(3), 031601 (Nov 27, 2013) (9 pages) Paper No: GTP-13-1093; doi: 10.1115/1.4025884 History: Received April 03, 2013; Revised October 20, 2013

This paper presents the metaheuristic design and optimization of fuzzy-based gas turbine engine (GTE) fuel flow controller by means of a hybrid invasive weed optimization/particle swarm optimization (IWO/PSO) algorithm as an innovative guided search technique. In this regard, first, a Wiener model for the GTE as a block-structured model is developed and validated against experimental data. Subsequently, because of the nonlinear nature of GTE, a fuzzy logic controller (FLC) strategy is considered for the engine fuel system. For this purpose, an initial FLC is designed and the parameters are then tuned using a hybrid IWO/PSO algorithm where the tuning process is formulated as an engineering optimization problem. The fuel consumption, engine safety, and time response are the performance indices of the defined objective function. In addition, two sets of weighting factors for objective function are considered, whereas in one of them savings in fuel consumption and in another achieving a short response time for the engine is a priority. Moreover, the optimization process is performed in two stages during which the database and the rule base of the initial FLC are tuned sequentially. The simulation results confirm that the IWO/PSO-FLC approach is effective for GTE fuel controller design, resulting in improved engine performance as well as ensuring engine protection against physical limitations.

Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.


Jaw, L. C., and Mattingly, J. D., 2009, Aircraft Engine Controls: Design, System Analysis, and Health Monitoring, American Institute of Aeronautics & Astronautics, Reston, VA, p. 361.
Chipperfield, A. J., Bica, B., and Fleming, P. J., 2002, “Fuzzy Scheduling Control of a Gas Turbine Aero-Engine: A Multiobjective Approach,” IEEE Trans. Ind. Electron., 49(3), pp. 536–548. [CrossRef]
Diao, Y., and Passino, K. M., 2002, “Adaptive Neural/Fuzzy Control for Interpolated Nonlinear Systems,” IEEE Trans. Fuzzy Syst., 10, pp. 583–595. [CrossRef]
Jong-Wook, K., and Woo, K. S., 2003, “Design of Incremental Fuzzy PI Controllers for a Gas-Turbine Plant,” IEEE/ASME Trans. Mechatron., 8(3), pp. 410–414. [CrossRef]
Watanabe, A., Olcmen, S. M., and Leland, R. P., 2006, “Soft Computing Applications on a SR-30 Turbojet Engine,” Fuzzy Sets Syst., 157, pp. 3007–3024. [CrossRef]
Branke, J., Deb, K., and Miettinen, K., 2008, Multiobjective Optimization: Interactive and Evolutionary Approaches, Springer-Verlag, Berlin, p. 470.
Basak, A., 2010, “A Modified Invasive Weed Optimization Algorithm for Time-Modulated Linear Antenna Array Synthesis,” IEEE Congress on Evolutionary Computation, Barcelona, July 18–23. [CrossRef]
Kennedy, J., and Eberhart, R., 1995, “Particle Swarm Optimization,” IEEE International Conference on Neutral Networks, Perth, WA, November 27– December 1, pp. 1942–1948. [CrossRef]
Goudos, S. K., Moysiadou, V., and Samaras, T., 2010, “Application of a Comprehensive Learning Particle Swarm Optimizer to Unequally Spaced Linear Array Synthesis With Sidelobe Level Suppression and Null Control,” IEEE Antennas Wireless Propagat. Lett., 9, pp. 125–129. [CrossRef]
Karimkashi, S., and Kishk, A. A., 2010, “Invasive Weed Optimization and Its Features in Electromagnetics,” IEEE Trans. Antennas Propagat., 58(4), pp. 1269–1278. [CrossRef]
Xiao, S., Bai, Y., and Liu, C., 2012, “A Hybrid IWO/PSO Algorithm for Pattern Synthesis of Conformal Phased Arrays,” IEEE Trans. Antennas Propagat, 61(4), pp. 2328–2332. [CrossRef]
Mehrabian, A. R., and Lucas, C., 2006, “A Novel Numerical Optimization Algorithm Inspired From Weed Colonization,” Ecolog. Informat., 1, pp. 355–366. [CrossRef]
Mehrabian, A. R., and Yousefi-Koma, A., 2007, “Optimal Positioning of Piezoelectric Actuators on a Smart Fin Using Bio-Inspired Algorithms,” Aerosp. Sci. Tech., 11, pp. 174–182. [CrossRef]
Rad, H. S., and Lucas, C., 2007, “A Recommender System Based on Invasive Weed Optimization Algorithm,” IEEE Congress on Evolutionary Computation, Singapore, September 25–28, pp. 4297–4304. [CrossRef]
Dadalipour, B., Mallahzadeh, A. R., and Davoodi-Rad, Z., 2008, “Application of the Invasive Weed Optimization Technique for Antenna Configurations,” Loughborough Antennas and Propagation Conference (LAPC 2008), Loughborough, UK, March 17–18, pp. 425–428. [CrossRef]
Mallahzadeh, A. R., Es'haghi, S., and Alipour, A., 2009, “Design of an E-Shaped MIMO Antenna Using IWO Algorithm for Wireless Application at 5.8 GHz,” Prog. Electromag. Res., 90, pp. 187–203. [CrossRef]
Mallahzadeh, A. R., Es'haghi, S., and Hassani, H. R., 2009, “Compact U-array MIMO Antenna Designs Using IWO Algorithm,” Int. J. RF Microw. CAE, 19(5), pp. 568–576. [CrossRef]
Zhang, X., Wang, Y., Cui, G., Niu, Y., and Xu, J., 2009, “Application of a Novel IWO to the Design of Encoding Sequences for DNA Computing,” Comput. Math. Appl., 57, pp. 2001–2008. [CrossRef]
Hajimirsadeghi, H., and Lucas, C., 2009, “A Hybrid IWO/PSO Algorithm for Fast and Global Optimization,” IEEE EUROCON 2009, St. Petersburg, Russia, May 18–23, pp. 1964–1971. [CrossRef]
Montazeri-Gh, M., and Jafari, S., 2011, “Evolutionary Optimization for Gain Tuning of Jet Engine Min-Max Fuel Controller,” J. Propul. Power, 27(5), pp. 1015–1023. [CrossRef]
Hoshino, Y., and Takimoto, H., 2012, “PSO Training of the Neural Network Application for a Controller of the Line Tracing Car,” IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Brisbane, Australia, June 10–15. [CrossRef]
Cai, L., Rad, A. B., and Chan, W. L., 2007, “A Genetic Fuzzy Controller for Vehicle Automatic Steering Control,” IEEE Trans. Veh. Tech., 56(2), pp. 529–543. [CrossRef]
Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., and Magdalena, L., 2004, “Ten Years of Genetic Fuzzy Systems: Current Framework and New Trends,” Fuzzy Sets Syst., 141(1), pp. 5–31. [CrossRef]
Yee, S. K., Milanovic, J. V., and Hughes, F. M., 2008, “Overview and Comparative Analysis of Gas Turbine Models for System Stability Studies,” IEEE Trans. Power Syst., 23(1), pp. 108–118. [CrossRef]
Kulikov, G. G., and Thompson, H. A., 2004, Dynamic Modeling of Gas Turbines, Springer, New York.
Montazeri-Gh, M., and Safarabadi, M., 2009, “Modeling and Simulation of Aero Gas Turbine Engine Performance for Fuel Control System Design,” IUST Int. J. Eng. Sci., 20(2).


Grahic Jump Location
Fig. 1

Schematic of the closed-loop control system

Grahic Jump Location
Fig. 2

Optimization strategy (tuning RB and DB sequentially)

Grahic Jump Location
Fig. 3

Encoding the DB parameters

Grahic Jump Location
Fig. 4

Encoding the RB parameters

Grahic Jump Location
Fig. 5

Rotor speed—comparison of model results to engine test data

Grahic Jump Location
Fig. 6

Membership functions for (a) first input, (b) second input, (c) output variable, case 2

Grahic Jump Location
Fig. 7

Transient fuel flow versus rotor speed error and its derivation, case 2

Grahic Jump Location
Fig. 8

Convergence of best and mean objective values in DB and RB optimization process, case 2

Grahic Jump Location
Fig. 9

The engine parameters before and after the optimization

Grahic Jump Location
Fig. 10

Rotor acceleration



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In