A Dynamic Fuzzy Neural Networks-Based Surface Vessels Course Tracking Controller

Yang Wang; Li Li Guo; Chen Guo

doi:10.4028/www.scientific.net/AMM.644-650.122

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650A Dynamic Fuzzy Neural Networks-Based Surface...

A Dynamic Fuzzy Neural Networks-Based Surface Vessels Course Tracking Controller

Abstract:

A Dynamic Fuzzy Neural Networks Course Tracking Controller (DFNNCTC) for Surface Vessels is presented to solve the uncertainties coursing by the wide and wave. A Dynamic Fuzzy Neural Networks (DFNN) combines with a PID controller to integrate the DFNNCTC, in which the structure and parameters are adjusted online, and the fuzzy rules are automatically generated when being trained. The intelligent algorithm conquers the disadvantage of either overfitting or overtraining in traditional static fuzzy neural networks-based control methods. Simulation results of a container’s course tracking control validate the effectiveness of the proposed algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

122-127

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.122

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Online since:

September 2014

Authors:

Yang Wang*, Li Li Guo, Chen Guo

Keywords:

Course Tracking Control, Dynamic Fuzzy Neural Networks, Surface Vessels, Uncertainty

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* - Corresponding Author

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