Nonlinear multivariable supervisory predictive control

Author

Liu, X.J. ; Niu, L.X. ; Liu, J.Z.

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Beijing, China

fYear

2009

fDate

10-12 June 2009

Firstpage

2779

Lastpage

2784

Abstract

The process of combined cycle power plant(CCPP) is characterized by nonlinearity and uncertainty. While model predictive control has been widely used in CCPP, incorporating of constraints is a major problem. Considering a supervisory control structure, this work presents nonlinear constraint predictive control by introducing of neuro-fuzzy networks(NFNs) representing a nonlinear dynamical process. Power and velocity control of gas turbine in CCPP is presented to illustrate the implementation and the performance of the proposed method. Comparative control studies suggest an improvement over conventional controller.

Keywords

combined cycle power stations; fuzzy neural nets; gas turbines; multivariable control systems; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; power control; predictive control; uncertain systems; velocity control; combined cycle power plant; gas turbine; neuro-fuzzy network; nonlinear dynamical process; nonlinear multivariable control; power control; supervisory predictive control; uncertain system; velocity control; Cost function; Economic forecasting; Nonlinear dynamical systems; Optimal control; Power generation; Power generation economics; Predictive control; Predictive models; Turbines; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2009. ACC '09.

Conference_Location

St. Louis, MO

ISSN

0743-1619

Print_ISBN

978-1-4244-4523-3

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2009.5160006

Filename

5160006