應用遞迴類神經網路模式於船舶動態系統鑑定與操縱性能預測之研究

Study on Using Recursive Neural Networks for System Identification of Ship Dynamics and Maneuverability Prediction

張敦理(Tun-Li Chang)

指導教授 ： 邱逢琛

國立臺灣大學/工學院/工程科學及海洋工程學研究所/碩士(2004年)

https://doi.org/10.6342/NTU.2004.01242

摘要

近年來，遞迴類神經網路(Recursive Neural Network; RNN)模式應用在船舶或水下載具運動之類的時變非線性動態系統模擬上的良好適用性已逐漸被認知，因此對於船舶操縱運動模擬與操縱性能預估，除了傳統上的流體動力模式或反應模式之外，提供了另一項可能的選擇。本研究的目的即在於應用RNN模式方法建構一適用於一般商船操縱運動模擬的RNN模式架構，並探討其在操縱性能預估上的有效性。
本研究提出的RNN模式由輸入層、兩個隱藏層及輸出層所構成。輸入層除了來自輸出層縱移、橫移速度和平穡仇t度的回饋之外，尚包含以簡易力學關係式表示的五項分力，即螺槳轉速與舵角引致的縱向推力、側向力，縱向速度與橫移速度引致的Munk 力矩，以及連同平穡仇t度共同引致的向心力之縱向分力及側向分力。而且為反應短期記憶效應，輸入資料包含了數個時步之前的數據。不同於傳統上的流體動力模式，本RNN模式不需用到任何未定的流體動力參數。本研究利用本所已發展完成，以日本MMG流體動力模式為基礎的船舶操縱運動模擬計算程式進行多組模擬計算產生對應的操船輸入與輸出資料以作為實船測試數據之替代，供試船則選用一艘全長192公尺的油輪。
研究顯示所提出的RNN模式架構作為一般商船操縱運動模擬與操縱性能預估工具是有效的，但是模擬計算過程中縱使航速預估的誤差頗小，仍會誘導出在時間軸上的延遲，此點有待今後進一步的探討與改進。本研究亦探討了為訓練RNN模式以掌握船舶操縱性能所需最小量的實測項目，以供實船測試規劃之參考。

關鍵字

船舶動態 ； 操縱性能 ； 遞迴類神經網路 ； 系統鑑定

並列摘要

In general, ship maneuvering motions are treated as the responses of a time dependent system modeled by nonlinear equations of motions. However, since a few years ago, recursive neural networks technique has been demonstrated applicable for simulating the maneuvers of naval ships as well as that of submarines. Therefore, in order to simulate maneuvering motions and predict maneuverability of a commercial ship, the method of using recursive neural networks modeling may be also available besides the traditional methods such as using hydrodynamic modeling or response modeling.
In this study, a recursive neural network model is developed and applied to simulate the maneuvers of a 192 meter long tanker, which may have inherent poor course stability. In the present model, lateral forces due to rudder angle and centrifugal force, longitudinal forces due to propeller thrust and centrifugal force, as well as Munk moment, used as the inputs of the recursive neural networks, are related to the input control variables such as ruder angle, propeller revolution and the output state variables such as motion velocities by very simplified functions without any undetermined hydrodynamic coefficients or empirical factors. The present recursive neural network is constructed with one input layer, one output layer and two hidden layers. Not only the above-stated forces, but also the outputs of surge velocity, sway velocity and yaw rate are fed back to the input layer of the network. In this study, the existing ship maneuver simulation program, which is developed basing on Japan MMG hydrodynamic model, is used for generating all the sample data of maneuvers for training and validating the recursive neural network.
As a result, although there is still some discrepancy on ship velocity prediction, it is shown that the present recursive neural network model is valid as a tool to simulate maneuvering motions and predict maneuverability for a commercial ship. Furthermore, the least sea trial data need to be obtained for training a recursive neural network and reflecting the maneuverability of a real ship is also discussed in this study.

並列關鍵字

ship dynamics ； maneuverability ； system identification ； recursive neural network

參考文獻

3. Hess, D., Faller, W.” Simulation of Ship Maneuvers Using Recursive Neural Networks”,” Proceedings of 23rd Symposium on Naval Hydrodynamics, Val De Reuil, France, September 2000, pp.160-175.

5. Haykin, S. “ Neural Networks: A Comprehensive Foundation”, Prentice-Hall, Inc., 2nd ed., 1999

8. Principe, J.C., Euliano, N.R., Lefebvre, W.C. ”Neural and Adaptive Systems – Fundamentals Through Simulations”, Hohn Wiley & Sons, Inc.,2000

參考文獻

1. Atlar, M., Mesbahi, E., Roskilly, A.P. and Gale, M.”Efficient Techniques in Time-Domain Motion Simulation Based on Artificial neural Network,” International Symposium on Ship Motions and Manoeuvrability, RINA, London, U.K., Feb. 1998, pp.1-23.