Iterative fuzzy model inversion

Annamaria R. Varkonyi-Koczy, Gabor Peceli, T. Dobrowiecki, Tamas Kovacshazy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

Nowadays model based techniques play very important role in solving measurement and control problems. Recently for representing nonlinear systems fuzzy models became very popular. For evaluating measurement data and for controller design also the inverse models are of considerable interest. In this paper a technique to perform fuzzy model inversion is introduced. The method is based on solving a nonlinear equation derived from the multiple-input single-output (MISO) forward fuzzy model simple by interchanging the role of the output and one of the inputs. The utilization of the inverse model can be either a direct compensation of some measurement nonlinearities or a controller mechanism for nonlinear plants. For discrete-time inputs the proposed technique provides good performance if the iterative inversion is fast enough compared to system variations, i.e. the iteration is convergent within the sampling period applied. The proposed method can be considered also as a simple nonlinear state observer, which reconstructs the selected input of the forward fuzzy model from its output using an appropriate strategy and a copy of the fuzzy model itself. It is also shown that using this observer concept completely inverted models can also be derived.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Editors Anon
PublisherIEEE
Pages561-566
Number of pages6
Volume1
Publication statusPublished - 1998
EventProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2) - Anchorage, AK, USA
Duration: May 4 1998May 9 1998

Other

OtherProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2)
CityAnchorage, AK, USA
Period5/4/985/9/98

Fingerprint

Controllers
Control nonlinearities
Nonlinear equations
Nonlinear systems
Sampling
Compensation and Redress

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

Cite this

Varkonyi-Koczy, A. R., Peceli, G., Dobrowiecki, T., & Kovacshazy, T. (1998). Iterative fuzzy model inversion. In Anon (Ed.), IEEE International Conference on Fuzzy Systems (Vol. 1, pp. 561-566). IEEE.

Iterative fuzzy model inversion. / Varkonyi-Koczy, Annamaria R.; Peceli, Gabor; Dobrowiecki, T.; Kovacshazy, Tamas.

IEEE International Conference on Fuzzy Systems. ed. / Anon. Vol. 1 IEEE, 1998. p. 561-566.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Varkonyi-Koczy, AR, Peceli, G, Dobrowiecki, T & Kovacshazy, T 1998, Iterative fuzzy model inversion. in Anon (ed.), IEEE International Conference on Fuzzy Systems. vol. 1, IEEE, pp. 561-566, Proceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2), Anchorage, AK, USA, 5/4/98.
Varkonyi-Koczy AR, Peceli G, Dobrowiecki T, Kovacshazy T. Iterative fuzzy model inversion. In Anon, editor, IEEE International Conference on Fuzzy Systems. Vol. 1. IEEE. 1998. p. 561-566
Varkonyi-Koczy, Annamaria R. ; Peceli, Gabor ; Dobrowiecki, T. ; Kovacshazy, Tamas. / Iterative fuzzy model inversion. IEEE International Conference on Fuzzy Systems. editor / Anon. Vol. 1 IEEE, 1998. pp. 561-566
@inproceedings{1f3b4c72c7f040eea6a37dfade839734,
title = "Iterative fuzzy model inversion",
abstract = "Nowadays model based techniques play very important role in solving measurement and control problems. Recently for representing nonlinear systems fuzzy models became very popular. For evaluating measurement data and for controller design also the inverse models are of considerable interest. In this paper a technique to perform fuzzy model inversion is introduced. The method is based on solving a nonlinear equation derived from the multiple-input single-output (MISO) forward fuzzy model simple by interchanging the role of the output and one of the inputs. The utilization of the inverse model can be either a direct compensation of some measurement nonlinearities or a controller mechanism for nonlinear plants. For discrete-time inputs the proposed technique provides good performance if the iterative inversion is fast enough compared to system variations, i.e. the iteration is convergent within the sampling period applied. The proposed method can be considered also as a simple nonlinear state observer, which reconstructs the selected input of the forward fuzzy model from its output using an appropriate strategy and a copy of the fuzzy model itself. It is also shown that using this observer concept completely inverted models can also be derived.",
author = "Varkonyi-Koczy, {Annamaria R.} and Gabor Peceli and T. Dobrowiecki and Tamas Kovacshazy",
year = "1998",
language = "English",
volume = "1",
pages = "561--566",
editor = "Anon",
booktitle = "IEEE International Conference on Fuzzy Systems",
publisher = "IEEE",

}

TY - GEN

T1 - Iterative fuzzy model inversion

AU - Varkonyi-Koczy, Annamaria R.

AU - Peceli, Gabor

AU - Dobrowiecki, T.

AU - Kovacshazy, Tamas

PY - 1998

Y1 - 1998

N2 - Nowadays model based techniques play very important role in solving measurement and control problems. Recently for representing nonlinear systems fuzzy models became very popular. For evaluating measurement data and for controller design also the inverse models are of considerable interest. In this paper a technique to perform fuzzy model inversion is introduced. The method is based on solving a nonlinear equation derived from the multiple-input single-output (MISO) forward fuzzy model simple by interchanging the role of the output and one of the inputs. The utilization of the inverse model can be either a direct compensation of some measurement nonlinearities or a controller mechanism for nonlinear plants. For discrete-time inputs the proposed technique provides good performance if the iterative inversion is fast enough compared to system variations, i.e. the iteration is convergent within the sampling period applied. The proposed method can be considered also as a simple nonlinear state observer, which reconstructs the selected input of the forward fuzzy model from its output using an appropriate strategy and a copy of the fuzzy model itself. It is also shown that using this observer concept completely inverted models can also be derived.

AB - Nowadays model based techniques play very important role in solving measurement and control problems. Recently for representing nonlinear systems fuzzy models became very popular. For evaluating measurement data and for controller design also the inverse models are of considerable interest. In this paper a technique to perform fuzzy model inversion is introduced. The method is based on solving a nonlinear equation derived from the multiple-input single-output (MISO) forward fuzzy model simple by interchanging the role of the output and one of the inputs. The utilization of the inverse model can be either a direct compensation of some measurement nonlinearities or a controller mechanism for nonlinear plants. For discrete-time inputs the proposed technique provides good performance if the iterative inversion is fast enough compared to system variations, i.e. the iteration is convergent within the sampling period applied. The proposed method can be considered also as a simple nonlinear state observer, which reconstructs the selected input of the forward fuzzy model from its output using an appropriate strategy and a copy of the fuzzy model itself. It is also shown that using this observer concept completely inverted models can also be derived.

UR - http://www.scopus.com/inward/record.url?scp=0031621699&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031621699&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0031621699

VL - 1

SP - 561

EP - 566

BT - IEEE International Conference on Fuzzy Systems

A2 - Anon, null

PB - IEEE

ER -