Search

• A novel anti-swing control method is proposed. • An anti-swing trajectory tracking error is reconstructed. • Time-varying workspaces and an anti-swing trajectory are forecasted. • Multistate sliding surface and multistate reaching law are presented. Shipborne multidimensional wave motion compensation devices eliminate the relative motion of cargo between supply and supplied ships. However, the random sway of ships and complicated disturbances still cause various cargo swings. Therefore, an anti-swing control method is proposed that combines deep learning prediction models with a multistate fractional-order terminal sliding mode controller for wave motion compensation devices. First, the constrained workspaces and anti-swing trajectories of cargo based on deep learning prediction models are constructed to decrease the swings of cargo originating from ship sway and inaccurate control parameters. Second, a multistate fractional-order terminal sliding mode controller is presented to eliminate the disturbance-caused cargo swings. Third, the stability of the multistate sliding mode controller is proven. Finally, the effectiveness of the anti-swing control method is verified through simulations and experiments, and the experimental results demonstrate that the maximum amplitude of cargo swings is only a few millimetres and that the maximum mean absolute error of the payload swings is less than four millimetres. [ABSTRACT FROM AUTHOR]

• A premonition-driven deep learning model is proposed, and two types of premonitory long short-term memory neural networks are constructed. • Hull motion premonition mechanism is presented, and premonition units are built based on countdown time variables. • Precursor information and local motion information are employed. Ship transit forecasting can provide advanced movement information to a multidimensional wave motion compensation system. However, traditional deep learning models based on data-driven or physical data have difficulty in accurately predicting the rapid variations in ship motion attitudes that frequently occur in complex ocean environments. Therefore, a premonition-driven deep learning model is proposed in this paper to significantly improve the prediction accuracy of ship violent roll motion. This model applies precursor information to predict premonitory values of ship prospective motion attitudes based on an integrated statistical regression model and then employs these premonitory values to revise the outputs of the deep learning time series model. First, a hull attitude premonition mechanism is presented based on biological premonition cognitive behaviour, which captures the correlation relationships between extreme values by downgrading the globally continuous time series prediction problem to a time-independent regression problem. The corresponding methods for predicting local extreme values and deep feedforward neural network cluster are established based on these correlation relationships between the extreme values to achieve precise premonitory values. Second, the time and amplitude variables of the extreme angle values are transformed to countdown time variables and sequential sets of premonitory angle values. Two types of premonition units and their respective premonitory long short-term memory neural networks are constructed to upgrade the regression problem to a globally continuous time series prediction problem. Finally, a single-step output recurrent strategy is applied to forecast the short-term ship motion attitude for fifteen seconds by considering eleven initially predicted points across the two datasets. In sixty-six simulation experiments, the mean absolute error, root mean squared error, and mean absolute percentage error of the premonition-driven long short-term memory neural networks are considerably lower than those of traditional deep learning models. These simulation experiments verify that the premonition-driven deep learning model can effectively forecast short-term violent ship roll motion and provide safeguards for cargo transfer. [ABSTRACT FROM AUTHOR]

Cable-rigid coupled wave motion compensation devices supply multi-dimensional anti-swing functions, while their trajectory and trajectory tracking accuracy are affected by random sway of ships and oceanic disturbances, and vibrations of cargoes exist as the characteristics of rigid-flexible coupling. Therefore, a multi-target and multi-state control method combining a deep learning prediction module with a multi-target and multi-state fractional-order sliding mode controller is proposed. First, advanced trajectory of the executive platform is constructed based on the deep learning prediction module to weaken impacts of the sway of ships and vibrations of the executive platform. Second, a multi-target and multi-state fractional-order mode controller is proposed, whose states are switched based on prediction errors, trajectory tracking errors and vibration amplitudes. Finally, the stabilities of the multi-target and multi-state fractional-order sliding mode controller are demonstrated, and the performances of the multi-target and multi-state control method are verified through simulations and experiments. • A multi-target and multi-state control method is proposed. • A multi-target and multi-state fractional-order sliding mode controller is developed. • Advanced trajectory is constructed based on a reformative deep learning prediction module. [ABSTRACT FROM AUTHOR]