海上氣候瞬息萬變，如何在海難發生後判斷並搜尋船隻位置是海上救援之首要課題。經統計，海上之事故發生頻率最高的船舶類型為漁業用船舶，其中機械故障、失火以及碰撞在發生事故種類中之發生機率即占總體一半以上，發生以上事故後之船隻將失去動力進行漂流，或被迫失去通訊能力進而無法求援。故本篇論文主要針對上述種類船隻，藉由求解流體動態方程式建構一套漂流估測方法，以增加海上救援救難之強度。
此漂流估測方法中，首先使用適應性模糊理論對具有相當複雜之偏微分特性流體動態方程組求解，經由實際量測之海流數據校正，利用適應性模糊理論兼具之內插特性求出並建立台灣周圍海域之海流模型。其後，根據船隻航行之行為建構出一組狀態空間表示式，根據動力學分析方法建立並設計回授線性化控制方法，模擬失去動力船隻在海上隨波逐流的實際情況。最後使用擴展性卡爾曼濾波器進行估測，進而得出失去動力船隻一段時間後之預估落點。此篇論文針對約二百公斤之小型船隻實行漂流估測方法驗證，分別進行長時間及短時間之漂流估測，估測誤差分別為4.5±3% 公里及 0.07±30% 公里，並討論若事故發生時當月海流數據缺失，以前一年之前後月份平均海流數據進行漂流估測之準確度。

In this paper, a position estimation method for precisely tracing the trajectory of ocean surface vessels under the situation of powerless and losing communication ability via the aid of the ocean current solution and nonlinear estimation method is proposed. The method procedure and contributions for the estimation is addressed as follows:
First, a group of momentum equations which is not easy to find the exact solution because of its complexity is proposed for ocean current modeling. The solution of these equations will be accurately solved by one new derived fuzzy approximation technology via a set of practical current observed data around Taiwan. The main contribution of this new modeling method can offer researchers one more useful and accurate ocean current history.
Second, a model formulated in state-space form for describing the drifting behavior of monitored ocean surface vessels is identified according to analysis the drifting behavior in practical situation from mechanical viewpoint.
Third, based on the above solution of ocean current and the identified model described the drifting behavior of monitored ocean surface vessel, a position estimation method realized with Extend Kalman filter are developed for the powerless drifting vessels, which are driven by the ocean current is investigated. The simulation errors are 4.5±3% kilometers and 0.07±30% kilometers which simulate via real incidents for two days and two hours, respectively, and discussing the accuracy of drift estimation via using the different ocean data of different months.

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