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...

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Vydáno v:	Control engineering practice Ročník 8; číslo 10; s. 1093 - 1105
Hlavní autoři:	Fink, Alexander, Fischer, Martin, Nelles, Oliver, Isermann, Rolf
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.10.2000
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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