System identification of Vessel Manoeuvring Models

Alexandersson, Martin; Mao, Wengang; Ringsberg, Jonas W.

doi:10.1016/j.oceaneng.2022.112940

Cited by 8 publications

(5 citation statements)

References 21 publications

(48 reference statements)

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“…The structures of these models are well-defined, and unknown parameters can be obtained by various system identification methods. One of the most popular parameter identification methods is the Kalman filter (KF) [22,23] and its variants, such as the extended Kalman filter (EKF) [24,25] and the unscented Kalman filter (UKF) [26][27][28]. Another popular method for parameter identification is the least squares (LS) method [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…where A d = e TsA ; B d = T s 0 e At dtB; A and B are the system dynamics and input matrices for the original continuous system, respectively; η is the noise uncorrelated with the input u; T s is the sampling period; and T s = 0.05 second (s) in the experiment. Model parameters can be derived directly using the LS algorithm, as follows: θ = ZH T (HH T ) −1 (25) where…”

Section: Model Identificationmentioning

confidence: 99%

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GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Zhang,

Li,

Xiong

et al. 2024

JMSE

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Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Model Identificationmentioning

confidence: 99%

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Zhang,

Li,

Xiong

et al. 2024

JMSE

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show abstract

“…To obtain the hydrodynamic derivatives described in Eq. ( 8), captive model tests and the VCMT can be employed as an entry for the mathematical model [33]. However, as these methods are costly, they were not used in this study.…”

Section: Hull Forces and Momentmentioning

confidence: 99%

“…However, their study is limited to control applications. Alexandersson et al [33] used advanced offline methods, such as the Extended Kalman Filter (EKF) with a Rauch Tung Striebel (RTS), to obtain the coefficients of linear, Abkowitz, and modified Abkowitz mathematical models for a twin-screw wind-powered car carrier (wPCC) with good stability and symmetrical hydrodynamic manoeuvring forces. Chen et al [34] presented an offline grey-box model based on the 4-DOF MMG model, utilizing the least square support vector machine (LS-SVM) method by implementing different levels of noise to the training datasets of SR108 container ship for parameter identification, and demonstrated the model's robustness under disturbance.…”

Section: Introductionmentioning

confidence: 99%

Application of an offline grey box method for predicting the manoeuvring performance

Atasayan,

Milanov,

Dursun Alkan

2024

Brodogradnja

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The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship.

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“…Modified versions of the classic least-squares algorithm are frequently used, for example, in the work of Zhao et al [30]. The extended Kalman filter (EKF) technique is used in many projects, for example, by Alexandersson et al [31].…”

mentioning

confidence: 99%

Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object

Koznowski,

Kula,

Lazarowska

et al. 2023

Electronics

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The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.

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System identification of Vessel Manoeuvring Models

Cited by 8 publications

References 21 publications

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Application of an offline grey box method for predicting the manoeuvring performance

Research on Synthesis of Multi-Layer Intelligent System for Optimal and Safe Control of Marine Autonomous Object

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