Next Article in Journal
Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments
Previous Article in Journal
Faster Force-Directed Graph Drawing with the Well-Separated Pair Decomposition
Article Menu

Export Article

Open AccessArticle

Control for Ship Course-Keeping Using Optimized Support Vector Machines

School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China
Fujian Province Key Laboratory of Structural Performances in Ship and Ocean Engineering, Fuzhou 350116, China
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2016, 9(3), 52;
Received: 17 May 2016 / Revised: 30 July 2016 / Accepted: 1 August 2016 / Published: 10 August 2016
(This article belongs to the Special Issue Data Analytics and Optimization for Hybrid Communication Systems)
PDF [1392 KB, uploaded 10 August 2016]


Support 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
Keywords: ship course-keeping; support vector machines; L2-gain design; uncertainties ship course-keeping; support vector machines; L2-gain design; uncertainties

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Luo, W.; Cong, H. Control for Ship Course-Keeping Using Optimized Support Vector Machines. Algorithms 2016, 9, 52.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top