Application of an On-line Trainning Predictor/Controller to Dynamic Positioning of Floating Structures

Tzung-hang Lee; Yusong Cao; Yen-mi Lin

doi:10.6180/jase.2001.4.3.01

Application of an On-line Trainning Predictor/Controller to Dynamic Positioning of Floating Structures

Tzung-hang Lee This email address is being protected from spambots. You need JavaScript enabled to view it.¹ , Yusong Cao² and Yen-mi Lin¹

¹Department of Mechanical Engineering Tamkang University Tamsui, Taipei, Taiwan 251, R. O. C.
²School of Naval Architecture and Marine Engineering University of New Orleans New Orleans, LA 70115, U. S. A.

Received: June 22, 2001
Accepted: July 24, 2001
Publication Date: September 1, 2001

Download Citation: ||https://doi.org/10.6180/jase.2001.4.3.01

ABSTRACT

An on-line training functional-link neural network predictor/controller for dynamic positioning of water surface structures is described in this paper. To develop a neural network for time-evolving systems, the deterministic on-line training model in a traditional parameter identification theory and the functional-link network are combined. The system’s previous input and output are used to be additional enhancements to the functional-link network. The on-line training neural network predictor acquires the knowledge about the system using a small number of samples of the latest system status measured on board of the structure. The trained functional-link neural network is used with an optimal controller to control the output of the system. The accuracy and robustness of the on-line training predictor are demonstrated through the numerical simulations of two ship maneuvers. The on-line training neural network predictor/controller is applied to the dynamic positioning (station-keeping) of a ship in a uniform current with and without external environmental disturbances. The results of the numerical simulations are very satisfactory.

Keywords: On-line Training, Neural Network Predictor/controller, Dynamic Positioning

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