Robust feedback linearization with neural network for underwater vehicle control

Author

Pollini, Lorenzo ; Innocenti, Mario ; Nasuti, Francesco

Author_Institution

Dipt. di Sistemi Elettrici Autom., Pisa Univ., Italy

Volume

1

fYear

1997

fDate

6-9 Oct 1997

Firstpage

12

Abstract

The present paper describes the synthesis of a velocity control system for a remote operated vehicle (ROV) using feedback linearization techniques. In order to improve robustness of the closed loop system with respect to modelling uncertainties, a neural network scheme is developed, based on mixed pattern-batch learning methods, and connected to the controller. The results are validated via nonlinear simulation, for a model of an existing vehicle

Keywords

closed loop systems; control system synthesis; feedback; linearisation techniques; marine systems; mobile robots; neurocontrollers; robust control; telerobotics; uncertain systems; ROV; closed loop system; feedback linearization techniques; mixed pattern-batch learning methods; modelling uncertainties; neural network; nonlinear simulation; remote operated vehicle; robust feedback linearization; underwater vehicle control; velocity control system; Closed loop systems; Control system synthesis; Linearization techniques; Network synthesis; Neural networks; Neurofeedback; Remotely operated vehicles; Robust control; Robustness; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

OCEANS '97. MTS/IEEE Conference Proceedings

Conference_Location

Halifax, NS

Print_ISBN

0-7803-4108-2

Type

conf

DOI

10.1109/OCEANS.1997.634327

Filename

634327