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dc.contributor.authorHuang, Ching-Hueien_US
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dc.description.abstract本論文的目的是在探討特徵系統實現理論運用於飛行載具氣動力模式之識別。本文採用線性及非線性之參數系統識別,以求得低速風洞試驗及高速風洞試驗下之載具氣動力參數。 文中設計之線性系統參數識別運用模糊特徵值系統整合風洞試驗系統中之各種變數,包括風洞風速、模型測試攻角、側滑角、水平尾角度、翼型及載具動力(馬達轉速及螺槳型式)等,與測試環境關聯性之相關實用模糊規則定義,進行小型動力無人飛行載具(power-on mini-UAV)及AGARD標準模型進行氣動力參數系統識別。本論文將模擬結果與小型動力無人飛行載具於低速風洞及AGARD標準模型於高速風洞吹試之數據進行比對,證實所提方法之效益。 由於風洞試驗中之各相關變數為高度非線性,故本研究亦運用多層遞迴式神經網路架構結合非線性特徵系統識別方法來探討飛行載具之氣動力參數。本非線性系統最大效益為可以直接引用該構型之線性特徵系統所求得歸屬函數值,定義為非線性之識別系統之初始權重,再利用遞迴式神經網路架構來學習最佳權重值,不需藉實際風洞吹試就能得到近似之飛行載具氣動力參數。 模擬結果初步地證實本線性系統與非線性系統之特徵系統實現理論均達到以較少之實際風洞試驗結果來有效地識別系統參數的目的。我們利用現有輸入參數及不同識別變數延伸到多層遞迴式神經網路架構,估計最佳氣動力參數,此不但可以減少實際風洞吹試需求,更能節約測試資源及冗長時間。zh_TW
dc.description.abstractThis 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.en_US
dc.description.tableofcontents中文摘要 I Abstract II Contents V List of Tables VII List of Figures VIII Chapter 1 Introduction 1 1.1 Motivations 1 1.2 Literature Review 4 1.2.1 Review of Eigenvalue Realization Algorithms (ERAs) 4 1.2.2 Review of Nonlinear Identification Based on Multilayer Recurrent Neural Network (MRNN) 6 1.3 Contributions of the Dissertation 7 1.4 Organization of the Dissertation 8 Chapter 2 Wind Tunnel System Descriptions 9 2.1 Low Speed Wind Tunnel 9 2.2 High Speed Wind Tunnel 11 2.3 Wind Tunnel Forces and Moments 12 2.4 Wind Tunnel Data Corrections 19 2.4.1 Balance Corrections 19 2.4.2 Wall Corrections 21 2.5 Measurement Errors 21 Chapter 3 Linear ERA-Based Model Identification 22 3.1 ERA 22 3.2 Fuzzified Eigenvalue Realization Algorithm (Fuzzified -ERA) 24 3.2.1 Fuzzy Logic Design 24 3.2.2 Fuzzy Correlation Coefficient 36 3.2.3 Fuzzified-ERA 37 3.3 Results and Discussions 39 3.3.1 Verification of Different Wind Tunnel Speed and Rotational Speed in LSWT 40 3.3.2 Verification of Different Angle of Elevator in LSWT 45 3.3.3 Verification of Different Wind Tunnel Speed in Higher-Order in LSWT 50 3.3.4 Verification of Different Wind Tunnel Speed in HSWT 56 3.4 Concluding Remarks 60 Chapter 4. Nonlinear Identification Based on Multilayer Recurrent Neural Network 61 4.1 Nonlinear RNN System Description 61 4.2 Nonlinear Parameter Identification Description 62 4.2.1 Learning Algorithm of RNN 67 4.2.2 Systemic error threshold 69 4.2.3 Learning rate 71 4.3 Nonlinear MRNN in ERA-Based Identification Algorithm 71 4.3.1 Nonlinear MRNN Description 72 4.3.2 Learning Algorithm of MRNN 75 4.3.3 Main Results 77 4.4 Results and Discussions 78 4.4.1 Verification of linear and nonlinear systems with RNN in HSWT 81 4.4.2 Verification of single variable with RNN in HSWT 84 4.4.3 Verification of linear and nonlinear systems with MRNN in LSWT 87 4.4.4 Verification of multivariable in MRNN with LSWT 92 4.5 Concluding Remarks 97 Chapter 5. Conclusions and Future Works 98 5.1 Conclusions 98 5.2 Future Works 99 Bibliography 100 Publication List 108zh_TW
dc.subjectEigensystem Realization Algorithm (ERA)en_US
dc.subjectFlight Vehicle Modelen_US
dc.subjectWind Tunnel Testen_US
dc.subjectNonlinear Identification Systemen_US
dc.subjectMultilayer Recurrent Neural Network (MRNN)en_US
dc.titleIdentification of Flight Vehicle Models Using Eigensystem Realization Algorithmen_US
dc.typeThesis and Dissertationzh_TW
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item.openairetypeThesis and Dissertation-
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