Publication detail

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

PIVOŇKA, P. VELEBA, V.

Original Title

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

Type

journal article - other

Language

English

Original Abstract

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain.

Keywords

Adaptive Controllers, Neural Networks for Identification, Comparison of Identifications methods, Rapid Sampling Domain.

Authors

PIVOŇKA, P.; VELEBA, V.

RIV year

2008

Released

12. 3. 2008

Publisher

www.naun.org

Location

USA

ISBN

1998-0140

Periodical

INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES

Year of study

1

Number

1

State

United States of America

Pages from

62

Pages to

67

Pages count

6

URL

http://www.naun.org

BibTex

@article{BUT44826,
  author="Petr {Pivoňka} and Václav {Veleba}",
  title="Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period",
  journal="INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES",
  year="2008",
  volume="1",
  number="1",
  pages="62--67",
  issn="1998-0140",
  url="http://www.naun.org"
}