Abstract
In this paper, a new prediction approach is proposed for ocean vessel heave compensation based on echo state network (ESN). To improve the prediction accuracy and enhance the robustness against noise and outliers, a generalized similarity measure called correntropy is introduced into ESN training, which is referred as corr-ESN. An iterative method based on half-quadratic minimization is derived to train corr-ESN. The proposed corr-ESN is used for the heave motion prediction. The experimental results verify its effectiveness.
Highlights
When operating on sea, a vessel is inevitably affected by waves, wind and ocean currents, thereby moving away from the desired position horizontally and vertically [1]
Some research result show that a controller with heave motion prediction is helpful in creating an active heave compensation (AHC) system, which results in 100% effectiveness in heave motion decoupling [2]
Heave compensation prediction based on esn with correntropy induced loss function researchers utilize autoregressive (AR), autoregressive moving average (ARMA) and moving average (MA) models to construct prediction model from time series for heave motion prediction
Summary
A vessel is inevitably affected by waves, wind and ocean currents, thereby moving away from the desired position horizontally and vertically [1]. The research works on heave motion prediction are not so much. Heave compensation prediction based on esn with correntropy induced loss function researchers utilize autoregressive (AR), autoregressive moving average (ARMA) and moving average (MA) models to construct prediction model from time series for heave motion prediction.
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