International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 2, No. 2, 8 Dec. 2010
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Parameters Nonlinear Estimation of the Propulsion System Performance Seeking Control Using Improved PSO
Full Text (PDF, 112KB), PP.31-37
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Author(s)
Yin Dawei
1,*
Liao Ying
1
Liang Jiahong
2
Index Terms
Aeroengine, component deviation parameters, nonlinear estimation, performance seeking control, particle swarm optimization, nonlinear equations, Kalman filter
Abstract
The estimation of aeroengine component deviation parameters (CDP) is an important portion of aeronautical propulsion system performance-seeking control (PSC), which employs linear Kalman filter based on piecewise state variable model (SVM) traditionally. But it’s not easy to get SVM, and the process of linearizing the nonlinear model to get the SVM will introduce errors. So parameters nonlinear estimation was introduced based on the nonlinear aeroengine model directly. The nonlinear estimation model is established according to aeroengine operation balance and the measured and calculated values matching of measurable parameters. The nonlinear estimation was changed to a problem of solving complex nonlinear equations, which is equal to an optimization problem. Time-varying inertia weight particle swarm optimization (PSO) with constriction factor was employed to solve the problem in order to satisfy the requirement of precision and calculation speed. The simulation results of a given turbofan engine show that utilizing the improved PSO algorithm can estimate the CPD precisely with satisfied converging speed.
Cite This Paper
Yin Dawei, Liao Ying, Liang Jiahong, "Parameters Nonlinear Estimation of the Propulsion System Performance Seeking Control Using Improved PSO", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.2, no.2, pp.31-37, 2010. DOI:10.5815/ijieeb.2010.02.05
Reference
[1]G. B. Gilyard and J. S. Orme, "Performance-seeking Control - Program Overview and Future Directions," AIAA-93-3765-CP 1993.
[2]J. S. Orme and T. R. Conners, "Supersonic flight test results of a performance seeking control algorithm on a NASA-15 spacecraft," AIAA 94-3210 1994.
[3]G. Alag and G. Gilyard, "A Proposed Kalman Filter Algorithm for Estimation of Unmeasured Output Variables for an F100 Turbfan Engine," AIAA-90-1920 1990.
[4]G. Kopasakis, "Nonlinear performance seeking control using Fuzzy Model Reference Learning Control and the method of Steepest Descent," in 33rd Joint Propulsion Conference & Exhibity cosponsored by AIAA,ASME,SAE, and ASEE Seattle/Washington, 1997.
[5]W. K. R and H. F. L, "GENENG-A program for calculating design and off-design performance for turbojet and turbofan engine," NASA TND-655 1972.
[6]L. Song-lin and S. Jian-gu, "Application of Genetic Algorithm to Solving Nonlinear Model of Aeroengines," CHINESE JOURNAL OF AERONAUTICS , vol. 16, 2003.
[7]J. Kennedy and R. Eberhart, "Particle Swarm Optimization," in IEEE Int. Conf. Neural Networks, Perth, Australia, 1995, p. 1942-1948.
[8]W. Elshamy, H. M. Emara, and A. Bahgat, "Clubs-based Particle Swarm Optimization," in 2007 IEEE Swarm Intelligence Symposium, 2007, p. 289-296.
[9]M. Clerc and J. Kennedy, "The Particle Swarm–Explosion, Stability, and Convergence in A Multidimensional Complex Space," IEEE Transactions on Evolutionary Computation, vol. 6, p. 58-73, 2002.
[10]N. Nakagawa, A. Ishigame, and K. Yasuda, "Particle swarm optimization with velocity control," IEEJ Transactions on Electrical and Electronic Engineering, vol. 4, p. 130-132, 2009.
[11]T. Cai, F. Pan, and J. Chen, "Adaptive Particle Swarm Optimization Algorithm," in Fifth World Congress on Intelligent Control and Automation, 2004. WCICA 2004, Hangzhou, P.R. China, 2004, p. 2245-2247.