Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM
-
Hanlin Sheng
and
Tianhong Zhang
Abstract
In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm – gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.
Acknowledgments
This work was supported by National Natural Science Foundation of China (No.51176075,No. 51576097), Funding of Jiangsu Innovation Program for Graduate Education (No.CXZZ13_0176).
References
1. Gastineau Z, Happawana G, Nwokah OD. Robust model-based control for jet engines. AIAA-98-3752, 1998.10.2514/6.1998-3752Search in Google Scholar
2. Maggiore M, Ordóñez R, Passino KM, Adibhatla S. Estimator design in jet engine applications. Eng Appl Artif Intel 2003;16(7):579–93.10.1109/CDC.1999.827974Search in Google Scholar
3. Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput 2004;14(3):199–222.10.1023/B:STCO.0000035301.49549.88Search in Google Scholar
4. Vapnik V. The nature of statistical learning theory. New York: Wiley, 2000.10.1007/978-1-4757-3264-1Search in Google Scholar
5. Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett 1999;9(3):293–300.10.1023/A:1018628609742Search in Google Scholar
6. Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inform Sci 2009;179(13):2232–48.10.1016/j.ins.2009.03.004Search in Google Scholar
7. Huerta EB, Duval B, Hao JK. A hybrid GA/SVM approach for gene selection and classification of microarray data. Applications of evolutionary computing. Berlin, Heidelberg: Springer, 2006:34–44.10.1007/11732242_4Search in Google Scholar
8. Pai PF, Hong WC. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energ Convers Manage 2005;46(17):2669–88.10.1016/j.enconman.2005.02.004Search in Google Scholar
9. Chapelle O, Vapnik V, Bousquet O, Mukherjee S. Choosing multiple parameters for support vector machines. Mach Learn 2002;46(1–3):131–59.10.1023/A:1012450327387Search in Google Scholar
10. Niu D, Wang Y, Wu DD. Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 2010;37(3):2531–9.10.1016/j.eswa.2009.08.019Search in Google Scholar
11. Melgani F, Bazi Y. Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans Inf Technol B 2008;12(5):667–77.10.1109/TITB.2008.923147Search in Google Scholar PubMed
12. Sulaiman MH, Mustafa MW, Shareef H, Khalid SNA. An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system. Int J Elect Power Energy Syst 2012;37(1):67–77.10.1016/j.ijepes.2011.12.007Search in Google Scholar
© 2017 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Effects of Cavity Configurations on Flameholding and Performances of Kerosene Fueled Scramjet Combustor
- Metaheuristic and Machine Learning Models for TFE-731-2, PW4056, and JT8D-9 Cruise Thrust
- Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria
- Use Deflected Trailing Edge to Improve the Aerodynamic Performance and Develop Low Solidity LPT Cascade
- A Co-modeling Method Based on Component Features for Mechatronic Devices in Aero-engines
- Evaluation and Analysis of Curvature-Corrected Filter-based Turbulent Model
- Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM
- Effect of Inner Nozzle Lip Thickness on Co-flow Jet Characteristics
- Comparisons of Two Non-probabilistic Structural Reliability Analysis Methods for Aero-engine Turbine Disk
Articles in the same Issue
- Frontmatter
- Effects of Cavity Configurations on Flameholding and Performances of Kerosene Fueled Scramjet Combustor
- Metaheuristic and Machine Learning Models for TFE-731-2, PW4056, and JT8D-9 Cruise Thrust
- Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria
- Use Deflected Trailing Edge to Improve the Aerodynamic Performance and Develop Low Solidity LPT Cascade
- A Co-modeling Method Based on Component Features for Mechatronic Devices in Aero-engines
- Evaluation and Analysis of Curvature-Corrected Filter-based Turbulent Model
- Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM
- Effect of Inner Nozzle Lip Thickness on Co-flow Jet Characteristics
- Comparisons of Two Non-probabilistic Structural Reliability Analysis Methods for Aero-engine Turbine Disk
