Vessel steering control using generalized ellipsoidal basis function based fuzzy neural networks

Authors:
Ning Wang;Zhiliang Wu;Chidong Qiu;Tieshan Li
Affiliations:
Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 2
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A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network

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Abstract

This paper contributes to vessel steering control system design via the Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF-FNN) method. Based on vessel motion dynamics and Nomoto model, a vessel steering model including dynamical K and T parameters dependent on initial forward speed and required heading angle is proposed to develop a novel dynamical PID steering controller including dynamical controller gains to obtain rapid and accurate performance. The promising GRBF-FNN algorithm is applied to dealing with the identification of dynamical controller gains. Typical steering maneuvers are considered to generate data samples for training the GEBF-FNN based dynamical steering controller while the prediction performance is checked by series of steering commands. In order to demonstrate the effectiveness of the proposed scheme, simulation studies are conducted on benchmark scenarios to validate effective performance.