Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification

Luo, Weilin

doi:10.1155/2016/8909170

Cited by 17 publications

(9 citation statements)

References 31 publications

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“…Dozens of coefficients are ought to be estimated by one time when applying the vectorial representation model, which is not a trivial job. The main reason is that the high correlation among a number of coefficients makes the parameter drift on estimated coefficients highly possible to occur (Luo 2016). If the model is switched to describe the dynamics of another different type of a ship, experiments on demand of much time and financial cost have to be done again.…”

Section: Requirements On the Modeling Of Ship Dynamicsmentioning

confidence: 99%

Optimized support vector regression algorithm-based modeling of ship dynamics

Zhu

Hahn

Wen

et al. 2019

Applied Ocean Research

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This thesis deals with the problem of how to derive a simplistic model feasible for describing the dynamics of ships for maneuvering simulations employed to study maritime traffic and to provide ship models for simulation-based engineering testbeds. The model should be expressed in a simple form with satisfactory accuracy as well as fast computation in simulations. The problem of deriving a ship dynamic model is addressed first with the modification and simplification of a complex vectorial representation in 6 degrees of freedom (DOF). The 6 DOF dynamic model is simplified through several pieces of reasonable assumptions that are exampled as ships moving in the horizontal plane in the ideal fluid, the uniform distribution of ship masses, the port-starboard symmetry. In the process of simplification, the trade-off between the accuracy and the possibility of estimation of the simplified model is regarded as the key criteria. Consequently, a 3 DOF dynamic model in a simple form with four terms for capturing surge motions and eight terms for steering motions is found, in which the reduced-term version of the steering model expressed in five terms is further obtained under the consideration of the There are many people who have earned my gratitude for their valuable contributions to my time at Oldenburg University. More specifically, I would like to thank my supervisor, Prof. Dr.-Ing. Axel Hahn, for his endless support and constant encouragement during my Ph.D. study in the SAMS program. He has given me inspiring guidance on how to conduct scientific research in modeling ship dynamics and system identification. Without his constant support and helpful guidance, this thesis would not have been possible. Axel is the funniest advisor and one of the smartest people I know. He always inspires me with his passionate attitude and forward-looking scientific insights. I would give Axel most of the credit for becoming the kind of scientist I am today. Besides, I am so grateful to Prof. Yuanqiao Wen, my second supervisor, who was also my master supervisor. He led me into the maritime-related scientific research field and taught me how to identify a research question, find a solution to it, and finally publish the results in high-level journals. Even more than 7km distance, he was still glad to provide insightful discussions and suggestions to me whenever I was in confusion of my research. I am also thankful to the members of the doctoral examination committee, Prof. Dr. Martin Fränzle and Dr. Lars Weber for their great support, impressive discussion and invaluable advice. I would also like to express my sincere gratitude to my friends and colleagues for helping me overcome difficulties from work and life. I do like to own my great gratitude to our secretary, Manuela Wüstefeld. I would never forget her warm-heart help and encouragement to me during these years especially the tough period when I was pregnant.

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Section: Requirements On the Modeling Of Ship Dynamicsmentioning

confidence: 99%

Optimized support vector regression algorithm-based modeling of ship dynamics

Zhu

Hahn

Wen

et al. 2019

Applied Ocean Research

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“…This phenomenon was observed by Hwang (Hwang, 1980) and was called parameter drift. The physical reason for the parameter drift was discussed by Hwang using slender-body theory and the multicollinearity was considered as the main factor (Hwang, 1980;Luo, 2016;Luo and Li, 2017). In this section, the classical parameters estimation method, Least Squares (LS), is briefly introduced, and a mathematical explanation of the parameter drift is discussed using singular value decomposition.…”

Section: Uncertainty Analysis Of the Estimated Hydrodynamic Coefficientsmentioning

confidence: 99%

Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests

Hassani

Soares

2019

Ocean Engineering

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Uncertainty analysis of the identified hydrodynamic coefficients of a nonlinear manoeuvring model is presented in this paper. The classical parameter estimation method, Least Square, is briefly introduced, and the uncertainty of the hydrodynamic coefficients due to the noise in the measured data is analysed using singular value decomposition. Then, two methods, truncated singular values decomposition and Tikhonov regularization, are introduced to diminish the uncertainty. A nonlinear manoeuvring mathematical model of a marine surface ship is derived using Lagrange's method. The dimensionless hydrodynamic coefficients are obtained using the Least Squares method, truncated singular values decomposition and Tikhonov regularization with Planar Motion Mechanism test data. The validation process is carried out to test the performance and accuracy of the resulting nonlinear manoeuvring models. The result shows that identification of the uncertain parameters using the truncated singular values decomposition and Tikhonov regularization resulted in good estimating the parameters and significantly diminish the uncertainty.

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“…Image reconstruction is an ill-posed problem, and it is generally known that Tikhonov regularization is an efficient way to solve ill-posed problems. Its basic idea is to transform equation (1) into an optimization problem [20][21][22][23][24]:…”

Section: Image Reconstructionmentioning

confidence: 99%

Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator

Liu

Ren

2019

Journal of Electrical and Computer Engineering

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Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise.

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Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification

Cited by 17 publications

References 31 publications

Optimized support vector regression algorithm-based modeling of ship dynamics

Optimized support vector regression algorithm-based modeling of ship dynamics

Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests

Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator

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