| 中文摘要 |
本文探討在不同洋流方向下，控制自主型水下載具（autonomous underwater vehicle，AUV）的推力，使載具保持欲航行的路徑，亦使其運動軌跡趨於穩定。藉由PMM（planar motion mechanism）試驗得到AUV之流體動力係數，提供動態數值模型進行六度運動模擬，以作為開發控制系統的基礎。本研究以自調式類神經網路比例微分（neural network proportional-derivative）控制器，控制推力來進行AUV的運動操控。在航跡控制方面，選用兩種不同方法，分別為瞄準線方式及瞬時深度控制法，並利用中繼點之技巧來改良AUV之軌跡穩定度。在洋流影響下，由結果驗證本研究發展之自調式類神經網路PD控制器配合改良式瞬時深度控制法，的確可穩定AUV運動姿態且達到快速導航之控制，並節省了傳統PD控制尋找最佳化增益參數所消耗之時間。
The main goal of the study is to find a feasible planning method of the track keeping for the autonomous underwater vehicle (AUV) and the motion behaviors of the AUV in different environments through the Artificial Neural Network (ANN) 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 L.O.S. (Line of Sight) technique is applied to guide the heading of the AUV and the PD (Proportional- Derivative) controller incorporating with the ANN algorithm is adopted to optimally control the thrust. 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 L.O.S. method, especially for the 3D track keeping problem. |
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