Control for Ship Course-Keeping Using Optimized Support Vector Machines

Luo, Weilin; Cong, Hongchao

doi:10.3390/a9030052

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…Notably, in the study no asymptotic stabilization could be obtained in the case of external disturbance, which can be theoretically confirmed by Equations ( 26) and (37). Nevertheless, as remarked on the L2-gain inequality (22), the errors in the stabilization system were bounded and could be as small as possible by decreasing γ 1 in Equations ( 28) and (38), and increasing λ 1 in Equation (30) and λ 2 in Equation (39). Further verification was conducted using a course-unstable surface ship [28], the training ship model Blue Lady, as shown in Figure 9.…”

Section: Example Studymentioning

confidence: 87%

“…As can be seen, the equality ( 21) contains the controller u 2 and the disturbance term, τ rw while the inequality (22) describes the performance of suppressing the disturbance τ rw . One can combine ( 21) with ( 22) in designing the controller u 2 to guarantee that the inequality (22)…”

Section: Controller Designmentioning

confidence: 99%

“…Among disturbance-suppression-based approaches, H ∞ control provides an effective method to suppress uncertain disturbances. As a method of H ∞ control, L2-gain design applies to nonlinear systems with disturbance and has been successfully used in roll stabilization [20,21] and course-keeping [22] of surface vessels. However, the applicability of this method to the stabilization of underactuated surface vessels with disturbances has not received attention.…”

Section: Introductionmentioning

confidence: 99%

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L2-Gain-Based Practical Stabilization of an Underactuated Surface Vessel

Luo

2021

JMSE

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To obtain a stabilizer for an underactuated surface vessel with disturbances, an L2-gain design is proposed. Surge, sway, and yaw motions are considered in the dynamics of a surface ship. To ob-tain a robust adaptive controller, a diffeomorphism transformation and the Lyapunov function are employed in controller design. Two auxiliary controllers are introduced for an equivalent sys-tem after the diffeomorphism transformation. Different from the commonly used disturbance ob-server-based approach, the L2-gain design is used to suppress random uncertain disturbances in ship dynamics. To evaluate the controller performance in suppressing disturbances, two error sig-nals are defined in which the variables to be stabilized are incorporated. Both time-invariant dis-continuous and continuous feedback laws are proposed to obtain the control system. Stability analysis and simulation results demonstrate the validity of the controllers proposed. A comparison with a sliding mode controller is performed, and the results prove the advantage of the proposed controller in terms of faster convergence rate and chattering avoidance.

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Section: Example Studymentioning

confidence: 87%

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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L2-Gain-Based Practical Stabilization of an Underactuated Surface Vessel

Luo

2021

JMSE

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“…Recall the above mentioned noticeable point about the requirement of dynamic models for ships, the former three models are not desirable fit. Trough the studies on the Nomoto model, it is reported to be applicable for control design especially the autopilot design in maritime engineering due to its simple structure and easy comprehension (Luo and Cong 2016) but its ability of capturing 1 DOF motions in yaw direction is not so expected to describe ship dynamics in particular used in ship´s motion simulation such as the MTS. Therefore, the task of how to match the requirement for the establishment of one dynamic model for different types of ships is deserved considerable study.…”

Section: Motivationmentioning

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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“…The polynomial kernel function is a global kernel function with strong generalization ability but weak learning ability [36], whereas the Gaussian radial basis kernel function is a local kernel function with strong learning ability but weak generalization ability. It is difficult to obtain good results in regression forecasting [37] by using only a single kernel function. Moreover, there are certain limitations in using the SVM with a single kernel function to predict the non-linear change in the data of the number of allocated containers of the container ship for one voyage.…”

Section: Construction Of Mixed Kernel Functionmentioning

confidence: 99%

A Forecast Model of the Number of Containers for Containership Voyage

2018

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Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage.

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Control for Ship Course-Keeping Using Optimized Support Vector Machines

Cited by 7 publications

References 38 publications

L2-Gain-Based Practical Stabilization of an Underactuated Surface Vessel

L2-Gain-Based Practical Stabilization of an Underactuated Surface Vessel

Optimized support vector regression algorithm-based modeling of ship dynamics

A Forecast Model of the Number of Containers for Containership Voyage

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