Control for Ship Course-Keeping Using Optimized Support Vector Machines
AbstractSupport vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller. View Full-Text
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Luo, W.; Cong, H. Control for Ship Course-Keeping Using Optimized Support Vector Machines. Algorithms 2016, 9, 52.
Luo W, Cong H. Control for Ship Course-Keeping Using Optimized Support Vector Machines. Algorithms. 2016; 9(3):52.Chicago/Turabian Style
Luo, Weilin; Cong, Hongchao. 2016. "Control for Ship Course-Keeping Using Optimized Support Vector Machines." Algorithms 9, no. 3: 52.
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