Supervision of nonlinear adaptive controllers based on fuzzy models

A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing...

Full description

Saved in:
Bibliographic Details
Published in:Control engineering practice Vol. 8; no. 10; pp. 1093 - 1105
Main Authors: Fink, Alexander, Fischer, Martin, Nelles, Oliver, Isermann, Rolf
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2000
Subjects:
Adaptive control
Heat exchanger
Nonlinear control
On-line adaptation
Recursive least squares (RLS)
Takagi–Sugeno fuzzy models
Variable forgetting factor
On-line adaptation
Recursive least squares (RLS)
Takagi–Sugeno fuzzy models
Heat exchanger
Variable forgetting factor
Adaptive control
Nonlinear control
ISSN:0967-0661, 1873-6939
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing unmodelled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi–Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation, a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of a heat exchanger.
ISSN:0967-0661
1873-6939
DOI:10.1016/S0967-0661(00)00059-9