Abstract
Online dynamic model identification is a promising technology for designing the model-based controller of marine crafts. This study confronts the online model identification and maneuvering prediction for marine crafts with an adaptive event-triggered mechanism. In particular, the least square-twin support vector machines algorithm is exploited to identify the dynamic model accurately. Meanwhile, an online adaptive event-triggered identification and prediction framework is proposed to deal with the issue of when to update the dynamic model. Finally, a comparative analysis of the identification and prediction performance between the proposed framework and the non-updated model method is performed. The former superiority is highlighted by experiments based on a marine craft, which shows that it reduces the root mean square error of the yaw rate by up to 45.9%. Thereby, the proposed online identification framework offers great potential for future practical applications of marine crafts.
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.
