Publication detail

Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates

VELEBA, V. PIVOŇKA, P.

Original Title

Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates

Type

journal article - other

Language

English

Original Abstract

In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. 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. Taking advantage of this result, we propose here an adaptive controller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller.

Key words in English

Rapid Sampling, Quantization, Neural Network, Training Set, Levenberg-Marquardt Minimization, Discrete PID Controller, RLS Identification Method

Authors

VELEBA, V.; PIVOŇKA, P.

RIV year

2005

Released

16. 6. 2005

ISBN

1109-2777

Periodical

WSEAS Transactions on Systems

Year of study

4

Number

4

State

United States of America

Pages from

385

Pages to

388

Pages count

4

BibTex

@article{BUT46303,
  author="Václav {Veleba} and Petr {Pivoňka}",
  title="Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates",
  journal="WSEAS Transactions on Systems",
  year="2005",
  volume="4",
  number="4",
  pages="4",
  issn="1109-2777"
}