Publication detail

Advantages of Neural Networks in Adaptive Control

SCHMIDT, M., PIVOŇKA, P.

Original Title

Advantages of Neural Networks in Adaptive Control

English Title

Advantages of Neural Networks in Adaptive Control

Type

conference paper

Language

en

Original Abstract

This paper discusses the problems adaptive controllers have to face when working with a sampling period that is significantly shorter than the global time constant of the controlled system. A short sampling period is beneficial for disturbance cancellation, but it makes on-line identification of the system difficult in the presence of quantization effect, noise and disturbances. Neural networks present a promising approach to solving the problem. However, there remains a problem with extracting useful information about the system's dynamics in the form of training patterns for commonly used regressive models. Ways to enrich the training patterns with information about the system's behaviour are discussed.

English abstract

This paper discusses the problems adaptive controllers have to face when working with a sampling period that is significantly shorter than the global time constant of the controlled system. A short sampling period is beneficial for disturbance cancellation, but it makes on-line identification of the system difficult in the presence of quantization effect, noise and disturbances. Neural networks present a promising approach to solving the problem. However, there remains a problem with extracting useful information about the system's dynamics in the form of training patterns for commonly used regressive models. Ways to enrich the training patterns with information about the system's behaviour are discussed.

Keywords

Adaptive Control, Neural Networks for Identification

RIV year

2006

Released

01.10.2006

Publisher

Rektor der Hochschule Zittau/Gorlitz

Location

Zittau

ISBN

3-9808089-8-X

Book

13th Zittau Fuzzy Coloquium

Pages from

75

Pages to

80

Pages count

6

BibTex


@inproceedings{BUT19696,
  author="Michal {Schmidt} and Petr {Pivoňka}",
  title="Advantages of Neural Networks in Adaptive Control",
  annote="This paper discusses the problems adaptive controllers have to face when working with a sampling period that is significantly shorter than the global time constant of the controlled system. A short sampling period is beneficial for disturbance cancellation, but it makes on-line identification of the system difficult in the presence of quantization effect, noise and disturbances. Neural networks present a promising approach to solving the problem. However, there remains a problem with extracting useful information about the system's dynamics in the form of training patterns for commonly used regressive models. Ways to enrich the training patterns with information about the system's behaviour are discussed.",
  address="Rektor der Hochschule Zittau/Gorlitz",
  booktitle="13th Zittau Fuzzy Coloquium",
  chapter="19696",
  institution="Rektor der Hochschule Zittau/Gorlitz",
  year="2006",
  month="october",
  pages="75",
  publisher="Rektor der Hochschule Zittau/Gorlitz",
  type="conference paper"
}