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系統識別號 U0026-2812201411044900
論文名稱(中文) 應用類神經網路運算法於自主式水下載具航跡控制之研究
論文名稱(英文) The Application of the Neuarl Network Algorithm on the Track Keeping Contorl of Autonomous Underwater Vehicle
校院名稱 成功大學
系所名稱(中) 系統及船舶機電工程學系
系所名稱(英) Department of Systems and Naval Mechatronic Engineering
學年度 103
學期 1
出版年 103
研究生(中文) 洪嘉祥
研究生(英文) Chia-Hsiang Hung
學號 P16011127
學位類別 碩士
語文別 中文
論文頁數 102頁
口試委員 指導教授-方銘川
共同指導教授-楊澤民
口試委員-王兆璋
口試委員-陳信宏
口試委員-林忠宏
中文關鍵字 潛航器  瞄準線  類神經網路  PD控制器 
英文關鍵字 AUV  Line of Sight  Neural Network algorithm  PD controller  Track keeping 
學科別分類
中文摘要 本論文旨在尋找一可行之自主型水下載具航跡控制方法,並探討其在不同環境因素底下經類神經網路系統之操控推力輸出後所表現出的運動性能。藉由控制自主型水下載具的推力,可使載具達到欲航駛的路徑,亦可讓其運動軌跡能趨於穩定。首先以前人作PMM(Planar Motion Mechanism)試驗可得的AUV之流體動力係數提供動態數值模擬程式來進行六度運動模擬,以作為開發控制系統的基礎,利用瞄準線方式找出AUV欲航行的方向,控制器方面選擇PD控制器,再加入類神經來控制PD控制器的比例因子,則以自調式類神經PD控制器控制推進器推力來進行AUV的運動操控,在作航跡控制時選用了兩種不同方法來作控制,一為以一般瞄準線方式來控制,二為以算出瞄準線後以每個時序XY平面AUV之位置與期望縱搖角所應對應到的Z座標來做深度控制,另外本文利用中繼點之技巧來改良AUV之軌跡穩定度。而本文也探討了有洋流情況下的影響。由模擬計算結果發現本文發展之自調式類神經PD控制器,一般的確可達到較佳且快速之控制並節省了許多傳統PD控制尋找最佳化增益參數所消耗之時間。
英文摘要 The main goal of the thesis is to find a feasible planning method of the track keeping for the AUV and the motion behaviors of the AUV in different environments through the Artificial Neural Network control system will be discussed. By controlling the AUV’s thrust, we can make the AUV’s motion be stable and reach its desired path. Based on the previous research of PMM (Planar Motion Mechanism) test for the AUV, we can provide the related hydrodynamic coefficients to the numerical computer program to solve the motion behaviors of the AUV, which can serve as the basis of the control system developed here. In the study, the LOS (Line of Sight) technique is applied to guide the heading of the AUV and the PD (Proportional- Derivative) controller incorporating with the Artificial Neural Network algorithm is adopted to allocate the proportional factors of the controller. Two kinds of the track keeping methods are adopted. The first is the traditional line of sight method and the second is applying the depth control based on the expected pitch angle. Both methods are also improved by using the step by step technique in order to obtain the more stable track keeping behaviors. Furthermore, the current effect is also included in the present study. From the present numerical simulation results, the neural network self-tuning PD controller is indeed more efficient on the AUV track keeping control than the traditional one. Besides, the second method with the expected depth control submitted here is also proved more stable than the traditional LOS method, especially for the 3D track keeping problem.
論文目次 中文摘要 I
Extended Abstract II
誌謝 V
目錄 VI
圖目錄 VIII
表目錄 XI
符號說明 XII
第一章 緒 論 1
1-1 研究動機與目的 1
1-2 文獻回顧 2
1-3 論文架構 5
第二章 潛航器動態方程式描述 6
2-1 大地座標系統與潛航器座標轉換關係式 7
2-2 推進器的推力與力矩 10
第三章 潛航器控制系統 12
3-1 傳統瞄準線(Line of Sight)控制法與瞬時深度控制法 12
3-1.1 中繼點 14
3-1.2 改良式傳統瞄準線控制法 16
3-1.3 改良式瞬時深度控制法 17
3-2 PD(Proportional-Derivative)控制 17
3-3 路徑點(Waypoint) 20
第四章 類神經網路控制理論 21
4-1 類神經網路簡介 21
4.1.1類神經網路模型 22
4-2 倒傳遞類神經網路 25
4-3 類神經網路運作流程 31
4-4 類神經網路架構 33
4-4.1 系統鑑別網路(NN1) 33
4-4.2 PD參數自調類神經網路(NN2) 38
第五章 結果與討論 45
5-1 同一平面不同方向角下之AUV運動反應 47
5-2 類神經PD控制器無洋流下之AUV運動 53
5-3 類神經PD控制器下洋流對AUV運動影響 65
5-4 自調式類神經PD控制器與傳統PD控制器下之比較 87
第六章 結論與建議 93
參考文獻 95
附錄一 AUV上配備資料 101
附錄二 傳統瞬時深度控制法 102
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