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Identification of Flight Vehicle Models Using Eigensystem Realization Algorithm
|關鍵字:||特徵系統實現理論;Eigensystem Realization Algorithm (ERA);飛行載具模型;風洞測試;非線性系統識別;多層遞迴式神經網路架構;Flight Vehicle Model;Wind Tunnel Test;Nonlinear Identification System;Multilayer Recurrent Neural Network (MRNN)||出版社:||電機工程學系所||引用:||D. P. Raymer, Aircraft Design: A Conceptual Approach, AIAA Education Series, 1992. F. O. Smetana, Flight Vehicle Performance and Aerodynamic Control, AIAA Education Series, 2001. J. N. Juang and R. S. Pappa, “ An Eigensystem Realization Algorithm (ERA) for Model Parameter Identification and Model Reduction,” Journal of Guidance, Control, and Dynamics, vol. 8, no. 5, pp. 620-627, Sept. 1985. F. J. P. Cebolla, A. Martinez, B. Martin, E. Laloya, C. E. Montano, S. Mendez and J. E. Vicuna, “Experimental equivalent circuit parameters identification of a switched reluctance motor,” in Proc. IEEE Annual Conference Industrial Electronics, pp. 1140-1145, Nov. 2009. C. W. Chen, J. K. Huang, M. Phan and J. N. 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This dissertation presents a new approach to deal with system identification for flight vehicle models using the eigensystem realization algorithm. The system identification is used to achieve the desired parameters of unmanned aerial vehicles (UAVs) in low-speed wind tunnel (LSWT) test and high-speed wind tunnel (HSWT) test.
The linear system identification applies a fuzzified eigensystem realization algorithm (fuzzified-ERA) for identification of the flight vehicle models in LSWT and HSWT. A variety of variables in model types and testing environment, such as tunnel wind speed, angle-of-attack, sideslip angle, elevator, mini-UAV model profile, and power system (motor and propeller) are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify effectiveness of the proposed methodology, simulations are conducted using the real-world experimental data that demonstrate the working performance of the proposed method correlates well as expected.
The relationship of variables for flight vehicles in wind tunnel test is highly nonlinear. To fulfill aerodynamic parameter identification of flight vehicle models, an eigensystem realization algorithm identification method of ERA based on a nonlinear multilayer recurrent neural network (MRNN) is also proposed. ERA is a mathematical method, which purpose is to use measurements observed over time, containing random variations and other inaccuracies from the input data, and produce values that tend to be closer to the true values. For the MRNN, it is included to estimate the optimal parameters of the nonlinear flight vehicle model. We apply the results of linear ERA membership function as the initial weights of the nonlinear ERA in RNN model parameter identification and determine the optimal weights to identify aerodynamic coefficients of flight vehicles with less testing. Simulation results preliminarily validate that the method resulted from linear and nonlinear system with eigensystem realization algorithms.
Considering the practical usefulness, the approaches presented in this dissertation efficiently help aerodynamicists selecting the optimal design parameters to meet the desired goal and reduce the cost for conducting real-world wind tunnel testing for flight vehicles.
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