Adaptive fuzzy generalized predictive control based on T-S model

Author

Zhang, Wei ; Li, Ping

Author_Institution

Sch. of Inf. Eng., Liaoning Univ. of Pet. & Chem. Technol., Fushun, China

Volume

1

fYear

2004

fDate

15-19 June 2004

Firstpage

694

Abstract

A T-S fuzzy model was established for nonlinear system by a fast fuzzy identification method based on fuzzy logic rules. The model parameters were modified by local recursive least square method at sampling point. According to the dynamic linearization model of the T-S fuzzy model, an adaptive fuzzy generalized predictive controller was designed. Compared with previous fuzzy generalized predictive controllers, the proposed controller is simple, and can be applied on-line. The simulation results show that this method is effective.

Keywords

adaptive control; control system synthesis; fuzzy control; fuzzy logic; fuzzy set theory; fuzzy systems; identification; least squares approximations; nonlinear control systems; predictive control; recursive estimation; sampling methods; T-S fuzzy model; adaptive fuzzy control; dynamic linearization model; fast fuzzy identification method; fuzzy logic rules; generalized predictive controller design; nonlinear system; recursive least square method; sampling point method; Adaptive control; Fuzzy control; Fuzzy logic; Fuzzy systems; Least squares methods; Nonlinear systems; Predictive control; Predictive models; Programmable control; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1340671

Filename

1340671