A robust adaptive neural networks controller for maritime dynamic positioning system

Authors:
Jialu Du;Yang Yang;Dianhui Wang;Chen Guo
Affiliations:
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, PR China;School of Information Science and Technology, Dalian Maritime University, Dalian 116026, PR China;Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia;School of Information Science and Technology, Dalian Maritime University, Dalian 116026, PR China
Venue:
Neurocomputing
Year:
2013

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Abstract

This paper aims to develop a neural controller using the vectorial backstepping technique for dynamically positioned surface ships with uncertainties and unknown disturbances. The radial basis function networks are employed to compensate for the uncertainties of ship dynamics and disturbances in controller design. The advantage of the proposed control scheme is that there is no requirement of any priori knowledge about dynamics of ships and disturbances. It is shown that our proposed control law can regulate the position and heading of ships to the desired targets with arbitrarily small positioning error. Theoretical results on stability analysis indicate that our proposed controller guarantees uniformly ultimate boundedness of all signals of the closed-loop system. Simulation studies with comparisons on a supply ship are carried out, and results demonstrate the effectiveness of the proposed control scheme.