本文應用類神經-模糊演算法於波浪中可攜式船舶動態定位控制系統，並描述配備此動態定位控制系統之駁船，在海洋環境外力下之非線性運動行為。此演算法可應用於控制推進器轉速，最佳化調整不規則波浪中船舶位置、航向與滿足船舶路徑跟蹤的需求，並可延伸發展至海底電纜敷設船的任務。類神經網絡或模糊控制，其共通優點是無需利用數學模式來推導控制器，可以大幅降低建構控制器所需的時間。其中，類神經網絡控制器利用模擬訓練的方式，得到適合各個海況下的動態增益值，可改善傳統比例微分（PD）控制中，需要反覆試誤得到控制器中的增益值。而模糊控制具有物理直觀的系統調整概念，在實際應用前，不需要大量模擬訓練。而結合這兩種演算法之控制系統將具有自我調整系統參數與節省模擬訓練時間的優點。除了波浪力外，計算中亦加入了潮流力、風力、纜繩力及二階漂移力。本研究利用四階Runge-Kutta方法去求解時域中配備可攜式動態定位系統之駁船六個自由度運動模擬。研究結果顯示，本文之類神經-模糊演算法應用於動態定位控制系統，在船舶頂浪與順浪的情況有相似的結果，並具有良好的定位效果，而在斜浪的情況下，船體運動較為明顯，使得艏向的定位誤差增大。而在路徑追蹤的例子中，路徑與航向誤差均維持在駁船動態定位的可接受的範圍。本文之貢獻在於突破使用單一固定增益值之動態定位控制系統的限制，並考慮船體耦合運動作用，可根據不同海況之變化，使用類神經-模糊演算法直接調整動態定位系統參數，增進定位控制系統之效率。對於無配備專業動態定位設備之船舶，有進行動態定位任務需求時，此演算法應用於可攜式動態定位系統將是一實際、有效的輔助工具。

The paper applies the Neuro-Fuzzy algorithm on the portable ship Dynamic Positioning (DP) control system in waves and describes the nonlinear dynamic motion behavior of the barge equipped with the portable outboard DP control system under the external forces in the ocean. The algorithm is applied to control the thrusters’ revolutions to optimally adjust the ship position, heading, and path-tracking in the ocean. The technique of the present DP control system is also used in the pipe or cable laying missions. Since it doesn’t need the mathematic models to derive the controllers, the Fuzzy control or Neural Network algorithm is therefore adopted here. The time consumption of building controller can then be decreased largely. Besides, after the training simulation, the neural network PD controller could obtain the suitable dynamic control gains for most sea states, which could replace the trial and error method to determine the gains in traditional PD controller. Moreover, the fuzzy control has the physical intuition in the system adjustment and need not to be trained in many cases before working in the real condition. Finally, the control system combining the two algorithms would have the advantages of self-tuning function and the training time saving. In addition to the waves, the external loading due to current, wind, cable and second-order drifting forces are also included in the calculations. The time domain simulations for the six degrees of freedom motions of the barge with the DP system are solved by the 4th order Runge-Kutta method. As to the head sea and following sea conditions, the neuro-fuzzy algorithm on the DP system indeed worked well. In the beam sea and oblique sea cases, because of the environmental forces, the barge heading varies significantly. And, in the path tracking cases, the results show that the path and heading deviation are limited in the acceptable ranges based on the present control method. The original contribution of this paper is to break through the limitations of traditional DP system using a single fixed gain value, and consider the coupling motion of the barge with DP system. According to the different sea states and the ship motions, the parameters of present DP system can be adjusted directly and DP capability is enhanced by the Neuro-Fuzzy algorithm. When the dynamic positioning missions are needed, the technique of the portable alternative DP system based the neuro-fuzzy algorithm can serve as a practical tool to assist those ships without equipping with the professional DP facilities.

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