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研究生:盧嘉弘
論文名稱:內模式控制架構之類神經船舶自航器設計
論文名稱(外文):An internal model control-based neural network ship steering autipilot design
指導教授:曾慶耀曾慶耀引用關係
學位類別:碩士
校院名稱:國立海洋大學
系所名稱:航運技術研究所
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
中文關鍵詞:平行內模式架構Norrbin非線性平擺方程式兩層前饋網路三層前饋網路倒傳遞演算法
外文關鍵詞:Parallel Internal Model ControlNorrbin Nonlinear Yaw ModelTwo-layer Feedforward NetworkThree-layer Feedforward NetworkBack Propogation Algorithm
相關次數:
  • 被引用被引用:7
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船舶於海上航行時,其運動行為受著流體流場變化而變化,很難以正確無誤用的數學模式描述出船舶所受流體的作用力與力矩,因此,一般於設計自航器時,通常使用簡化之模式,以利於控制器之設計及實現。但在設計控制系統時,必須考量真實受控體(plant)與受控體模式(model)之間模式誤差的變化。若能即時、有效地掌控系統操作點受環境變化的影響,來告知控制器,使其能適時地調變控制參數,使之作出正確的反應,如此控制效果將能有所提升。本文將結合平行內模式控制(internal model control,IMC)來作為控制迴路系統基本架構並利用類神經網路,來建構自航器控制器系統。其中之內模式架構,強調著受控體與受控體模式之間的關係,並具有易分析控制系統中的強健性與穩定性等特點。而類神經網路其具有學習適應功能,將作為動態系統建模,與反模式動態的建立。本文主要動機即期望能合併兩者的優點,來設計船舶自航器。本文分別利用了兩層前饋網路及三層前饋網路,來各別設計了平擺角速率自航器及航向自航器,並分別測試不同的參考訊號(方波、鋸齒波及正弦波)。經由模擬結果顯示本研究所提出之結合內模式控制架構,與類神經網路之自航器設計方法均能順利追蹤所給予之參考訊號,完成追蹤任務。
It is well known that the ship steering dynamics is characterized by a highly complicated nonlinear behavior. For simplicity, a linear model is often adopted in the design of the steering autopilot to facilitate the design and implementation. However, to make the autopilot of practical use, the modeling error between the model and the plant under control has to be monitored and the controller parameters should be adjusted accordingly.
In this work, the internal model control (IMC) configuration is adopted and the neural network (NN) is employed in describing the model and the controller, which is essentially the model inverse under the IMC structure. Two important features are combined in this study, specifically, the IMC has a clear connection between the model and the controller and the NN is capable of learning adaptively. In this work, both two-layer and three-layer feedforward networks are considered in the design of a heading control autopilot and a yaw rate control autopilot. Numerical simulations indicate that very good tracking performance is achieved for the square wave, saw-tooth and sine wave reference inputs.
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 1
1.3 研究動機 3
1.4 章節組織 3
第二章 船舶操縱運動數學模式 4
2.1 船舶操縱運動方程式 4
2.2 Norrbin非線性平擺方程式 7
2.3 操縱性能測試 9
第三章 平行內模式架構 14
3.1 平行內模式控制(IMC)迴路系統 14
3.2 平行內模式控制迴路與傳統閉迴路之差異 16
3.3 考量舵機限制及舵機速率限制 17
第四章 類神經網路 19
4.1 類神經網路 19
4.2 學習演算法-倒傳遞演算法 20
第五章 平行內模式架構與類神經網路之結合 24
5.1 合併平行內模式架構與類神經網路 24
5.2 適應性網路系統建模 25
5.3 適應性反模式網路訓練 26
5.4 網路鍵結值更新運算法 27
第六章 數值模擬結果與討論 30
6.1 線性網路設計平擺角速率自航器 32
6.2 線性網路設計航向自航器 38
6.3 非線性網路設計平擺角速率自航器 45
6.4 非線性網路設計航向自航器 53
6.5 結果與討論 61
第七章 結論與建議 63
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