Publication detail

Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control

PIVOŇKA, P. VELEBA, V. OŠMERA, P.

Original Title

Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control

English Title

Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control

Type

conference paper

Language

en

Original Abstract

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 and explain why should be neural network based identification better then classical by using of short sampling period. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. 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.

English abstract

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 and explain why should be neural network based identification better then classical by using of short sampling period. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. 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.

Keywords

Rapid sampling domain, Neural networks for identification, Comparison of identifications methods

RIV year

2007

Released

23.07.2007

Publisher

WSEAS

Location

Řecko

ISBN

978-960-8457-90-4

Book

Systems Theory and Applications

Edition

Vol. 2.

Edition number

1.

Pages from

217

Pages to

222

Pages count

6

BibTex


@inproceedings{BUT28312,
  author="Petr {Pivoňka} and Václav {Veleba} and Pavel {Ošmera}",
  title="Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control",
  annote="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 and explain why should be neural network based identification better then classical by using of short sampling period. The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. 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.",
  address="WSEAS",
  booktitle="Systems Theory and Applications",
  chapter="28312",
  edition="Vol. 2.",
  institution="WSEAS",
  year="2007",
  month="july",
  pages="217--222",
  publisher="WSEAS",
  type="conference paper"
}