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    Title: 應用基因演算法於無人飛行載具之系統識別與最佳化設計
    Other Titles: System identification and optimization of unmanned aerial vehicle by genetic algorithm
    Authors: 陳宣辰;Chen, Hsuan-Chen
    Contributors: 淡江大學航空太空工程學系碩士班
    張永康;Chang, Yeong-Kang
    Keywords: 無人飛行載具;基因演算法;系統識別;unmanned aerial vehicle;Genetic Algorithm;system identification
    Date: 2011
    Issue Date: 2011-12-28 19:18:34 (UTC+8)
    Abstract: 無人飛行載具之結構系統識別問題為將有限元素分析模型之分析值與實際模型之測試值的誤差減為最小。本研究利用振動實驗儀器擷取無人飛行載具之訊號與數據,並匯入OR25與Star system模態分析軟體量測無人飛行載具之測試值,以及應用ANSYS有限元素分析軟體對無人飛行載具之電腦模型進行動態分析來取得分析值。本研究將系統識別問題轉換為最佳化問題,並利用基因演算法全域隨機搜尋之特性使得在求解過程中最佳解不至於落入區域最佳解之中。本研究利用增減節點的集中質量修正有限元素模型,並且將ANSYS中的APDL語法與FORTRAN程式結合成一系統程式,利用量測出來之測試值修正有限元素模型,使其之間的誤差減小以期達到良好的系統識別。
    由數值分析範例之結果,證明了運用基因演算法於系統識別問題的確能夠有效的減少測試值與分析值之間的誤差,使得有限元素模型之特性更接近真實結構。修正後的有限元素模型更具有分析上之意義。
    The objective of system identification is to correlate the finite element data and modal test data of the Unmanned Aerial Vehicle (UAV) in this study. The modal test data and analysis data of UAV are obtained by vibration test experiment and ANSYS software respectively. Mathematically, the structural system identification problem is identical to optimum design problem. The objective function of system identification problem is to minimize the difference of analysis/test natural frequency. Therefore, the system identification problem can be solved by Genetic Algorithm. The advantage of Genetic Algorithm is that it can jump over the local optimum and obtain the global optimum. The grid lumped mass was used as design variable to modify the finite element model. A systematic process is developed by combining APDL of ANSYS and FORTRAN program.
    Numerical examples will be demonstrated the ability of Genetic Algorithm to solve system identification problem. The characteristics of UAV finite element model are similar to test model after modification.
    Appears in Collections:[航空太空工程學系暨研究所] 學位論文

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