Přístupnostní navigace
E-application
Search Search Close
Publication detail
PIVOŇKA, P. VELEBA, V. OŠMERA, P.
Original Title
Using of Neural Network Based Identification for Short Sampling Period in Adaptive Control
Type
conference paper
Language
English
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.
Keywords
Rapid sampling domain, Neural networks for identification, Comparison of identifications methods
Authors
PIVOŇKA, P.; VELEBA, V.; OŠMERA, P.
RIV year
2007
Released
23. 7. 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", booktitle="Systems Theory and Applications", year="2007", series="Vol. 2.", number="1.", pages="217--222", publisher="WSEAS", address="Řecko", isbn="978-960-8457-90-4" }