Motion predictive control for DPS using predicted drifted ship position based on deep learning and replay buffer

Lee, Daesoo; Lee, Seung-Jae

doi:10.1016/j.ijnaoe.2020.09.004

Cited by 18 publications

(3 citation statements)

References 11 publications

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“…Wave, current, and wind loads affect a ship's movement [4]. To accurately simulate the actual working conditions of the ship, first, the actual wave, current, and wind data of the target sea area were obtained from the major meteorological data platforms, and then, the effective data were used to calculate the change laws of the wave, current, and wind; next, an environment disturbance model were constructed according to the statistical laws, and finally, the environment disturbance model were used as the disturbance item to study the performance of the ship tracking the target path.…”

Section: Environmental Disturbance Modelmentioning

confidence: 99%

Ship Path Tracking with Real Disturbance and Exponential Cost Function

Zhang¹,

Li²,

Liu³

et al. 2023

Preprint

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Section: Environmental Disturbance Modelmentioning

confidence: 99%

Ship Path Tracking with Real Disturbance and Exponential Cost Function

Zhang¹,

Li²,

Liu³

et al. 2023

Preprint

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“…Nevertheless, RNN models often encounter challenges with vanishing or exploding gradients when dealing with lengthy sequences due to gradient propagation through time steps during backpropagation process resulting in gradual decrease or increase in gradients. LSTM partially mitigates this issue by introducing gate mechanisms [10,11]; however, it still exhibits limitations in capturing long-term dependencies within time series when confronted with extended input sequences.…”

Section: Introductionmentioning

confidence: 99%

Dynamic chaos unveiled: enhancing ship’s attitude time series prediction through spatiotemporal embedding and improved transformer model

Zhao,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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During ship operations at sea, the vessel's attitude undergoes continuous changes due to various factors such as wind, waves, and its own motion. These influences are challenging to mathematically describe, and the changes in attitude are also influenced by multiple interconnected factors. Consequently, accurately predicting the ship's attitude presents significant challenges. Previous studies have demonstrated that phenomena like wind speed and wave patterns exhibit chaotic characteristics when affecting attitude changes. However, research on predicting ship attitudes lacks an exploration of whether chaotic characteristics exist and how they can be described and applied. This paper initially identifies the chaotic characteristics of ship attitude data through phase space reconstruction analysis and provides mathematical representations for them. Based on these identified chaotic characteristics, aTransformer model incorporating feature embedding layers is employed for time series prediction. Finally, a comparison with traditional methods validates the superiority of our proposed approach.

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“…Large maritime constructions like floating offshore platforms and special engineering vessels that float in the water might typically have their location and spatial attitude determined by empirical calculations based on heading angles, velocity, and ocean currents [1][2][3][4]. In fact, the changes in a ship's position that can be roughly calculated by empirical formulas are not timely and not accurate enough, and for some construction projects that require high accuracy and fast feedback speed, they are far from meeting their technical requirements [5][6][7]. This is due to the instantaneity of wind, wave, and current load changes in the time domain, the randomness and mutability of changes in the height direction, and the changes in a ship's position that can be roughly calculated by empirical formulas [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response of DP Offshore Platform-Riser Multi-Body System Based on UKF-PID Control

Zhang

Zhao

Bai

et al. 2022

JMSE

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The coupling effect between the offshore platform and the riser in the offshore platform-riser multi-body system might be greatly increased under heavy external maritime stresses. The system will become significantly more nonlinear. The partial secondary development of OrcaFlex is carried out considering the strong non-linearity of the dynamic positioning (DP) offshore platform-riser multi-body system, combined with the actual offshore construction engineering background and the lumped mass method, based on Python-language embedded programming with the basis of the operation principle of the application program interface (API) and the composition of its modules. To regulate the dynamic positioning of the offshore platform-riser multi-body coupling system, a UKF-PID control approach based on an unscented Kalman filter is presented. Based on the procedures described above, a classical calculation model is created, and the model’s calculation results are compared to those of relevant references, confirming the method’s validity and viability. Finally, the model of the PID-controlled dynamic positioning offshore platform-riser rigid–flexible multi-body system is developed, and a dynamic simulation is performed under specified sea conditions. The findings have implications for engineering practice.

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Motion predictive control for DPS using predicted drifted ship position based on deep learning and replay buffer

Cited by 18 publications

References 11 publications

Ship Path Tracking with Real Disturbance and Exponential Cost Function

Ship Path Tracking with Real Disturbance and Exponential Cost Function

Dynamic chaos unveiled: enhancing ship’s attitude time series prediction through spatiotemporal embedding and improved transformer model

Dynamic Response of DP Offshore Platform-Riser Multi-Body System Based on UKF-PID Control

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