Adaptive Neural-net Control System for Ship Roll Stabilization

In this paper, an adaptive neural-net control system, in which learning is performed in a loop totally independent from the control loop, is proposed for the problem of ship roll stabilization. The modeling of the ship and the controller are adjusted continuously in order to deal with changes of dyn...

Full description

Saved in:
Bibliographic Details
Published in2007 IEEE International Conference on Control Applications pp. 735 - 740
Main Authors Xuejing Yang, Xiren Zhao, Xiuyan Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Adaptive control
Adaptive systems
Control system synthesis
Control systems
Fuzzy control
Linear feedback control systems
Marine vehicles
Mathematical model
Motion control
Programmable control
Online AccessGet full text

Cover

More Information
Summary:In this paper, an adaptive neural-net control system, in which learning is performed in a loop totally independent from the control loop, is proposed for the problem of ship roll stabilization. The modeling of the ship and the controller are adjusted continuously in order to deal with changes of dynamic properties caused by disturbances. Based the experimental data in tank, disturbance model caused by sea wave is presented. A recurrent neural network is used to approaching the dynamics of the ship, and the real time recurrent learning algorithm is described to train the forward model. This paper proposes the adaptation process of control system and applies it to the ship HD702. The control effect of roll stabilization and the approaching accuracy of forward model network are investigated.
ISBN:9781424404421
1424404428
ISSN:1085-1992
2576-3210
DOI:10.1109/CCA.2007.4389320