Model Predictive Control of a Shipborne Hydraulic Parallel Stabilized Platform Based on Ship Motion Prediction

Qiang, Hongbin; Song, Jinling; Feng, Xinyu; Xue, Dapeng; Zhang, Lijie

doi:10.1109/access.2020.2992458

Cited by 17 publications

(5 citation statements)

References 24 publications

(25 reference statements)

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“…Fuentes extensively discussed various machine learning prediction techniques [24], encompassing regression models such as the Linear Regression Model (LRM) [25], the autoregressive model (AR) [26], Support Vector Regression (SVR), Gaussian Process Regression (GPR), neural networks like artificial neural networks (ANN), as well as the Kalman Filter (KF) and Random Forest (RF). These models typically require ship velocity, acceleration, heading, and position data for training.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Sedaghat,

Arbabkhah,

Jafari Kang

et al. 2024

JMSE

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“…LRM and SVR can effectively complete tasks, and other regression models also perform well in ship trajectory prediction, such as autoregressive prediction (AR) and Gaussian process regression (GPR). Qiang et al [34] used the AR model to predict ship motion, offering a ship stabilization platform model predictive control approach based on ship motion prediction. A better option than Bayesian linear regression is Gaussian process regression (GPR).…”

Section: Other Regression Modelsmentioning

confidence: 99%

Artificial Intelligence in Ship Trajectory Prediction

Bi,

Cheng,

Zhang

et al. 2024

JMSE

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Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.

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“…Due to the advantages of parallel robots in terms of payload capacity, stiffness, and response speed [2], they are well-suited for carrying instruments and equipment on ships, compensating for the dynamic changes in their attitude. Whether the shipborne stabilized platform can effectively compensate for the impact of vessel motion on onboard equipment depends, on the one hand, on the stability control of the stabilized platform.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Calibration for the 3-UPS/S Shipborne Stabilized Platform Based on Transfer Learning

Xu,

Tian,

Zhang

2024

JMSE

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The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to compensate for pose errors and enhance motion accuracy. However, collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks. In this study, a “transfer network” is established by combining fine-tuning with a Back Propagation (BP) neural network. This network takes the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transfers them to a network trained based on the actual poses. Compared with the conventional BP neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of small-sample measured data. Considering this, the impact pattern of the sample number of the actual pose on the effectiveness of transfer learning is revealed through the construction of multiple transfer network models under varying sample numbers of the actual pose, providing valuable marine engineering guidance. Finally, simulated sea-service experiments were conducted on the 3-UPS/S shipborne stabilized platform to validate the correctness and superiority of the proposed method.

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Model Predictive Control of a Shipborne Hydraulic Parallel Stabilized Platform Based on Ship Motion Prediction

Cited by 17 publications

References 24 publications

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data

Artificial Intelligence in Ship Trajectory Prediction

Kinematic Calibration for the 3-UPS/S Shipborne Stabilized Platform Based on Transfer Learning

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