臺灣博碩士論文加值系統

本論文以模糊卡爾曼濾波器為理論基礎進行載具姿態之估測，研究中並針對所建立之估測演算法進行模擬與實驗。本研究之慣性導航系統，係採用四組三軸加速規，以正交輔助型模式配置，並以四元數法描述載具之三維姿態。故在估測載具姿態時可減少一次積分，避免積分誤差累積。使用卡爾曼濾波器之前提，必須對狀態雜訊和量測雜訊之協方差矩陣型式在能完全掌握的情況下，方能估測出載具之姿態結果為最佳。因此本研究使用模糊邏輯，提高雜訊估測之精確性，並避免演算過程中產生發散。經由程式模擬與運動平台姿態判定之實驗，結果證明，本研究所採用的架構與方法確能達成三維載具姿態判定的目標。

In this thesis the attitude determination of vehicles is investigated based on fuzzy Kalman filter theory, and the estimation algorithm is tested by simulation programs and by experiments. The inertia navigation system developed in this research adopts four sets of tri-axial accelerometers which are arranged by the complementary orthogonal configuration. The system describes the spatial attitude of a vehicle using the quaternion representation. The advantage of the above system is that one less integration is needed in the estimation process, so as to avoid the accumulation of integration error. One prerequisite for Kalman filter is that the information of the process noise covariance and measurement noise covariance should be known in advance in order to derive best estimation result. Hence the fuzzy logic is used in this research to enhance the accuracy of noise approximation, so that the computational divergence can be avoided. The algorithm is tested under numerical simulation and experiments on the attitude estimation of a motion platform. The simulation and experimental results show that the algorithm is feasible and satisfactory.

目錄
誌謝…………………………………………………………..…………Ⅰ
中文摘要…………………………………………………..……………Ⅱ
英文摘要……………………………………………………..…………Ⅲ
目錄………………………………………………………..……………Ⅳ
圖目錄……………………………………………………..……………Ⅶ
表目錄……………………………………………………..……………Ⅹ

第一章 緒論…………………………………………….…….………..1
1.1 前言……………………………………………….….………..1
1.2 文獻回顧……………………………………….…….………..1
1.3 研究動機與目的………………………………….….………..3

第二章 理論背景………………………………………….…..……….4
2.1 慣性導航基本原理………………………………..….………4
2.2 慣性感測器……………………………………….….…….…4
2.3 姿態表示法……………………………………….….…….…4
2.3.1 方向餘弦矩………………………………..…….……4
2.3.2 尤拉角……………………………...……..…….…….6
2.3.3 四元數法…………………………………………..….8
2.4 加速規判定載具三軸姿態法……………………………..…11
2.5 加速規配置模型…………………………………………..…11
2.5.1 正交型……………………………………………..….11
2.5.2 正交輔助型………………………………..………….14
2.6 姿態估測演算法………………………….……….…………17
2.6.1 卡爾曼濾波器原理……………………….…….……17
2.6.2 線性隨機系統之狀態空間模型……….……….……18
2.6.3 擴增卡爾曼濾波器……………………………..……22
2.7 載具三維姿態估測演算法之設計 ………….……….…….24
2.8 模糊理論 ……………………………………….…….…….33
2.8.1 模糊適應性卡曼濾波……….……………..………...34
第三章 實驗設備………………………………….……………….…..38
3.1 運動測試平台……………………………….………………39
3.2 量測子系統……………………………….…………………42
3.2.1 加速規………………………………….…………….42
3.2.2 姿態與航向參考系統…………………….………….44
3.3 擷取與資料傳送子系統………………….…………………45
3.4 運算監控子系統…………………………….………………47
第四章 模擬、實驗之結果與討論 …………………….…………….48
4.1 程式模擬結果……………………………….………………48
4.1.1 單軸運動………………………………….………….48
4.1.2 多軸運動………………………………….………….53
4.2 實驗結果…………………………………….………………52
4.2.1 靜止運動……………………………………….…….56
4.2.2 單軸運動…………………………………………..…58
4.2.3 多軸運動………………………………..……………61
4.3 討論 ……………………………………………………..….64
第五章 結論與未來展望…………………………………………..…66
參考文獻…………………………………………………………..……68

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