Abstract
Abstract The paper describes an adaptive neural network based sliding mode control for fin roll stabilization of ocean motoryachts. Radial basis function neural networks are used to adaptively learn system uncertainty bounds and network outputs are used to adjust the gain of the sliding mode control. The analysis of the control stability is based on the Lyapunov theory. The proposed controller is tested by a simulation study based on a nonlinear model, describing the dynamics of a vessel in four degrees of freedom. On this manoeuvring model output sea state disturbances are simulated as multisine time series.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
