Neural network modeling and generalized predictive control for an autonomous underwater vehicle

Author

Xu, Jianan ; Mingjun Zhang

Author_Institution

Coll. of Mech. & Electr. Eng., Harbin Eng. Univ., Harbin

fYear

2008

fDate

13-16 July 2008

Firstpage

487

Lastpage

491

Abstract

This paper investigates the application of neural network based generalized predictive motion control to an autonomous underwater vehicle. The modified Elman neural network is used as the multi-step predictive model, the fused identification model is proposed to improve the predictive and control precision. The modified Elman neural network on-line learning improves the control system adaptability to the unpredicted operating environment for autonomous underwater vehicle. Simulations on autonomous underwater vehicle yaw velocity control are included to illustrate the effectiveness of the proposed control scheme.

Keywords

adaptive control; learning (artificial intelligence); motion control; neurocontrollers; predictive control; remotely operated vehicles; underwater vehicles; velocity control; Elman neural network online learning; autonomous underwater vehicle; control system adaptability; fused identification model; generalized predictive motion control; multistep predictive model; neural network modeling; yaw velocity control; Artificial neural networks; Control systems; Motion control; Neural networks; Nonlinear dynamical systems; Open loop systems; Predictive control; Predictive models; Underwater vehicles; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on

Conference_Location

Daejeon

ISSN

1935-4576

Print_ISBN

978-1-4244-2170-1

Electronic_ISBN

1935-4576

Type

conf

DOI

10.1109/INDIN.2008.4618149

Filename

4618149