Neural-network-based near-time-optimal position control method for DC motor servosystems

Author

Yen, V. ; Liu, T.Z. ; Liu, D.Y.

Author_Institution

Dept. of Mech. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan

Volume

142

Issue

5

fYear

1995

fDate

9/1/1995 12:00:00 AM

Firstpage

493

Lastpage

500

Abstract

The paper considers the development and implementation of a near-time-optimal neural-network-based position control method for DC motor servosystems. To bypass the difficulties caused by system constraints and modelling uncertainties, the paper uses classification neural networks to learn the time-optimal control law from experimentally generated near-time-optimal trajectories. In addition, by using regression neural networks to learn the relationship between control object displacement and the armature voltage pulse-width, a variable-pulsewidth control strategy is developed to achieve accurate positioning. Experimental results are given to demonstrate the effectiveness of the proposed approach

Keywords

DC motors; learning (artificial intelligence); machine control; neurocontrollers; pattern classification; position control; servomechanisms; statistical analysis; time optimal control; DC motor servosystems; accurate positioning; armature voltage pulse-width; classification neural networks; control object displacement; neural-network-based near-time-optimal position control; regression neural networks; variable-pulsewidth control strategy;

fLanguage

English

Journal_Title

Control Theory and Applications, IEE Proceedings -

Publisher

iet

ISSN

1350-2379

Type

jour

DOI

10.1049/ip-cta:19952021

Filename

468431