Evolving Fuzzy Model-based Adaptive Control

Author

De Barros, Jean-Camille ; Dexter, Arthur L.

Author_Institution

Oxford Univ., Oxford

fYear

2007

fDate

23-26 July 2007

Firstpage

1

Lastpage

5

Abstract

The paper describes an evolving fuzzy model-based adaptive controller (eMAC) that is suitable for use in non-linear, uncertain systems. Two fuzzy models are used to predict the future behaviour of the plant; one is an evolving T-S fuzzy model that is learnt online from normal operating data; the other is a fixed T-S fuzzy model that is identified off-line from data obtained from a generic linear model of the plant to be controlled. The controller is applied to a simple non-linear dynamic system that has a significant time delay and simulation results are presented which demonstrate that the evolving fuzzy model-based adaptive controller does improve the performance of the control system. The controller is now to be tested experimentally on the air temperature control loop of a cooling coil in a real air-handling unit.

Keywords

adaptive control; delays; fuzzy control; nonlinear control systems; T-S fuzzy model; adaptive control; nonlinear control system; nonlinear dynamic system; Adaptive control; Delay effects; Fuzzy control; Fuzzy systems; Nonlinear control systems; Nonlinear dynamical systems; Predictive models; Programmable control; Temperature control; Uncertain systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International

Conference_Location

London

ISSN

1098-7584

Print_ISBN

1-4244-1209-9

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2007.4295552

Filename

4295552