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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This study contributes to developing a novel hybrid identification method based on intelligent algorithms, i.e. the least support vector regression algorithm (LS-SVR) and the artificial bee colony algorithm (ABC), to deal with the identification of the simplified ship dynamic model while the outliers exist in the measurements. The ship dynamic model is directly derived from our previous work which has been well verified and validated. The outliers are detected by introducing the robust estimation method namely the 3σ principle and then deleted from the training data. The weighted version of LS-SVR (WLS-SVR) with spareness and robustness ability is used as the fundamental identification approach. To improve the performance of the WLS-SVR, the structural parameters involved in it are optimized by utilizing the artificial bee colony algorithm (ABC), and the weights of it are adaptively set with the use of the adaptive weight method. Two case studies including the simulation study on a container ship and the experimental study on an Unmanned Surface Vessel (USV) are carried out to test the proposed hybrid intelligent identification method. The simulation study demonstrates the effectiveness and the acceptable time complexity in terms of the engineering application of the proposed identification method through the comparison with the cross-validation method and particle swarm optimization algorithm optimized LS-SVR. In the experimental study, ABC-LSSVR, ABC-LSSVR with the 3σ principle (D-ABC-LSSVR), ABC-LSSVR with the adaptive weight (ABC-AWLSSVR), and ABC-LSSVR with both the 3σ principle and the adaptive weight (D-ABC-AWLSSVR) are applied to identify the steering model for the USV. The results indicate that the influence of the outliers on model identification is effectively diminished by the robust 3σ principle and the adaptive weight method and that the D-ABC-AWLSSVR outperforms over the other three identification methods in terms of the mean squared error (MSE) of the model predictions. INDEX TERMS Ship dynamics modeling, outlier detection, robust 3σ principle, adaptive weight, artificial bee colony algorithm, least square support vector regression algorithm, a hybrid intelligent identification method. MAN ZHU was born in Huangpi, Hubei, China, in 1989. She received the B.S. degree in maritime safety administration and the M.S. degree in traffic information engineering and control from the

Identification of parameters involved in the linear response model with high precision is a highly cost-effective, as well as a challenging task, in developing a suitable model for the verification and validation (V+V) of some key techniques for autonomous vessels in the virtual testbed, e.g., guidance, navigation, and control (GNC). In order to deal with this identification problem, a novel identification framework is proposed in this paper by introducing the extended state observer (ESO), and the well-evaluated robust weighted least square support vector regression algorithm (RW-LSSVR). A second-order linear response model is investigated in this study due to its wide use in controller designs. Considering the highly possible situation that only limited states could be measured directly, the required but immeasurable states in identifying parameters contained in the response model are approximately estimated by the ESO. Theoretical analysis of the stability is given to show and improve the applicability of the ESO. Simulation studies based on linear response models with predefined parameter values of a cargo vessel and a patrol vessel maneuvering in an open water area are carried out, respectively. Results show that the proposed approach not only estimates immeasurable states with high accuracy but also ensures good performance on the parameter identification of the response model with very close values to the nominal ones. The proven identified approach is economic because it only requires limited kinds of low-cost sensors.

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