A Real-Time Sequential Ship Roll Prediction Scheme Based on Adaptive Sliding Data Window

A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun's full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.

I. Introduction

Ship’s roll motion is vital for navigational efficiency and marine safety. The prediction of ship roll is also necessary for ship operations such as maneuvering in rough sea and operating of air vehicles onboard the ship. However, ship roll motion in the sea is a complex process with time-varying dynamics and uncertainties which are caused by various influential factors [1]. Ship roll dynamics vary with changes of navigational status such as sailing speed, trim and loading conditions [2]. Moreover, variations are also influenced by environmental factors like winds [3], currents and waves [4]. Ship’s roll and other types of ship movements, such as heave [5] and pitch [6] motions are influenced and coupled with each other [7]. Therefore, the process of ship motion is complex and difficult to be precisely represented by deterministic models [8], [9]. To get precise predictions for real-time ship roll dynamics, an adaptive nonlinear model is crucially needed.