Adaptive Backstepping Neural Network approach to ship course control

Author

Meziou, M. Taktak ; Ghommam, J. ; Derbel, N.

fYear

2011

fDate

22-25 March 2011

Firstpage

1

Lastpage

7

Abstract

This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The control design uses estimate values of unknown parameters of the system. Then, adaptive laws of the estimation of these values have been proposed. The stability of the controlled system has been ensured by the use of a Lyapunov function. Simulation results show the effectiveness of the proposed approach and the designed controller can be applied to the ship course tracking with good performances.

Keywords

Lyapunov methods; adaptive control; control system synthesis; neurocontrollers; nonlinear control systems; position control; radial basis function networks; ships; stability; Lyapunov function; adaptive backstepping neural network method; adaptive estimation law; control design; control system stability; ship course tracking control; third order nonlinear model; Adaptation model; Artificial neural networks; Backstepping; Convergence; Equations; Lyapunov methods; Marine vehicles; Adaptive Backstepping Neural Network; Gaussian radial basis function neural network; nonlinear control;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Devices (SSD), 2011 8th International Multi-Conference on

Conference_Location

Sousse

Print_ISBN

978-1-4577-0413-0

Type

conf

DOI

10.1109/SSD.2011.5767389

Filename

5767389