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研究生:徐伯熊
研究生(外文):Po-Hsiung Hsu
論文名稱:基因演算於飛行軌跡重建之應用
論文名稱(外文):The Application of Genetic Algorithms in Flight Trajectory Reconstruction
指導教授:陸鵬舉陸鵬舉引用關係
指導教授(外文):Pong-Jeu Lu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:航空太空工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:中文
論文頁數:100
中文關鍵詞:飛行軌跡重建基因演算法擴展型卡氏濾波器最大能性估測
外文關鍵詞:Flight Trajectory ReconstructionGenetic AlgorithmsExtended Kalman FilterMaximum Likelihood Estimation
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本研究主旨在以基因演算法(Genetic Algorithms)解決傳統飛行軌跡重建時識別系統誤差參數所產生的數值僵化(Numerical Stiffness)問題。飛行軌跡重建是利用飛行時所記錄的參數數據,進行飛行器系統狀態變數估測(State Estimation)後積分而得。數據記錄會因感測器的老化或未做校正而產生訊號偏差(Bias),使重建出的飛行軌跡不準確。為了解決上述問題,所以我們需針對系統誤差參數及系統的狀態變數同時進行估測。本文為提出利用基因演算估測系統誤差參數,並以擴展型卡氏濾波器(Extended Kalman Filter)估測系統狀態變數的研究。由最大可能性原理(Maximum Likelihood Principle)推導可獲得一目標函數(Objective Function),經優化此目標函數即可獲得系統誤差參數的最佳估測。利用基因演算所估測的系統誤差參數結果雖與真實參數值仍有誤差,但所獲得的系統狀態變數估測與飛行軌跡較傳統以梯度為基礎的優化方法準確。此外,本研究更利用基因演算的優點同時估測系統六自由度全向運動誤差參數。此參數識別方法同時兼具有強健(Robust)與高精確的特性,未來除了可應用於飛航安全之外,更可作為精確飛行器空氣動力參數識別及飛行控制器設計之用。
The objective of this research is to solve the numerical stiffness problem encountered system error parameter identification and flight trajectory reconstruction using genetic algorithms. The flight trajectory, however, was reconstructed by time marching the aircraft state variables after parameter correction. In the practical applications, sensor bias may exist due to material aging or poor calibration, resulting in inaccurate trajectory reconstruction. This work attempts to use genetic algorithms in conjunction with extended kalman filter to identify the system error parameters and estimate the state variables. The objective function is derived using maximum likelihood principle, and the system error parameters are obtained by optimizing the objective function. It is shown that the state variables and flight path estimated by the genetic algorithm approach were more accurate than the traditional gradient-based methods. In addition, the robustness is also achieved. The advantages of genetic algorithms in estimating the system error parameter pertaining to the six degree-of-freedom full motions at one time were also demonstrated. It is demonstrated that the original numerical stiffness problem is completely circumvented using genetic algorithms. The success of the present approach can be extended to identify the derivatives and the design of flight controller as well.
目 錄中文摘要………………………………………………………………………i英文摘要………………………………………………………………………ii誌謝…………………………………………………………………………iii目錄……………………………………………………………………………iv表目錄………………………………………………………………………viii圖目錄…………………………………………………………………………ix符號說明………………………………………………………………………xi第一章 簡介…………………………………………………………………11-1 前言………………………………………………………………………11-2 研究動機與目的…………………………………………………………21-3 飛試數據參數識別方法回顧……………………………………………31-3-1 方程式誤差法…………………………………………………………41-3-2 輸出誤差法……………………………………………………………51-3-3 濾波器誤差法…………………………………………………………61-4 研究內容…………………………………………………………………6第二章 飛行動力系統建立……………………………………………………82-1 飛行動力系統簡述………………………………………………………82-2 六自由度動力系統………………………………………………………102-3 觀察子方程式……………………………………………………………152-4 空氣動力係數模型………………………………………………………172-5 軌跡重建方程式…………………………………………………………18第三章 參數識別與狀態估測………………………………………………193-1 模型方程式………………………………………………………………193-2 濾波器誤差法……………………………………………………………213-2-1 飛行數據相容性測試…………………………………………………213-2-2 最大可能性估測………………………………………………………243-2-3 擴展型卡氏濾波器……………………………………………………273-2-4 優化方法………………………………………………………………283-3 參數識別的困難…………………………………………………………30第四章 基因演算……………………………………………………………324-1 基因演算興起與應用……………………………………………………324-2 基因演算特點……………………………………………………………334-3 編碼型基因演算…………………………………………………………354-3-1 物種形式………………………………………………………………354-3-2 二進位型基因演算基本操作步驟……………………………………364-4 實數型基因演算…………………………………………………………404-4-1 物種形式………………………………………………………………404-4-2 實數型基因演算基本操作步驟………………………………………414-5 基因演算的數學基礎模式理論…………………………………………424-5-1 複製對於字串模式的影響……………………………………………434-5-2 交換對於字串模式的影響……………………………………………444-5-3 突變對於字串模式的影響……………………………………………46第五章 基因演算於飛行軌跡重建之應用…………………………………485-1 飛行數據模擬……………………………………………………………485-2 程式驗證…………………………………………………………………495-2-1 擴展型卡氏濾波器程式驗證…………………………………………495-2-2 基因演算程式驗證……………………………………………………495-3 系統誤差參數識別………………………………………………………505-3-1 縱向運動系統誤差參數識別…………………………………………525-3-2 橫向運動系統誤差參數識別…………………………………………535-3-3 六自由度運動系統誤差參數識別……………………………………54第六章 結論…………………………………………………………………566-1 結論………………………………………………………………………566-2 未來發展…………………………………………………………………57附錄……………………………………………………………………………58參考文獻………………………………………………………………………59表………………………………………………………………………………62圖………………………………………………………………………………66
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