Nonlinear Identification for 4-DOF Ship Maneuvering Modeling via Full-Scale Trial Data

Song, Chunyu; Zhang, Xianku; Zhang, Guoqing

doi:10.1109/tie.2021.3062255

Cited by 27 publications

(7 citation statements)

References 15 publications

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“…where K 0 is the gain coefficient of the ship model and T 1 , T 2 , T 3 are the ship model time constants. Converting this transfer function to a state space expression, in order to facilitate the inclusion of wind and wave disturbances, matrix transformations according to the Hankel norm and equilibrium realization definitions [21,22] can derive the standard form mathematical model in Equation ( 13):…”

Section: Ship Kinematics Modelmentioning

confidence: 99%

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

Song,

Guo,

Sui

et al. 2024

JMSE

Self Cite

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In order to solve the problem of the dynamic positioning control of large ships in rough sea and to meet the need for fixed-point operations, this paper proposes a dynamic positioning controller that can effectively achieve large ships’ fixed-point control during Level 9 sea states (wind force Beaufort No. 10). To achieve a better control effect, a large ship’s forward motion is decoupled to establish a mathematical model of the headwind stationary state. Meanwhile, the closed-loop gain shaping algorithm is combined with the exact feedback linearization algorithm to design the speed controller and the course-keeping controller. This effectively solves the problem of strong external interferences impacting the control system in rough seas and guarantees the comprehensive index of robustness performance. In this paper, three large ships—the “Mariner”, “Taian kou”, and “Galaxy”—are selected as the research objects for simulation research and the final fixing error is less than 10 m. It is proven that the method is safe, feasible, practical, and effective, and provides technical support for the design and development of intelligent marine equipment for use in rough seas.

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Section: Ship Kinematics Modelmentioning

confidence: 99%

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

Song,

Guo,

Sui

et al. 2024

JMSE

Self Cite

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“…In the three research fields of modern control systems (state estimation, identification, and control), system identification can effectively model the control process of uncertain or time-varying systems. The most commonly used identification methods are the least squares method [10], least squares recursive method [11,12], least squares support vector machine [13,14], random gradient algorithm [15], and neural networks based on identification algorithms [16]. Zhang, Zou [17], and Xu [18] used LSSVM and ε-LSSVM to research the modeling and prediction of ship maneuvering black boxes; Luo Weilin [19] used LSSVM to identify ship maneuverability parameters.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism

Song

Zhang

2022

JMSE

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This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models.

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“…First, ship dynamics are time-varying [4] and nonlinear [6]. Time-varying environmental factors, such as waves [7], currents [8], and winds [9], can directly affect ship motions. Second, ship motions are highly coupled [10] and have chaos characteristics [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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Nonlinear Identification for 4-DOF Ship Maneuvering Modeling via Full-Scale Trial Data

Cited by 27 publications

References 15 publications

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

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