Nonlinear fuzzy supervisory predictive control based on genetic algorithm

Author

Li Suzhen ; Liu Xiangjie ; Yuan Gang

Author_Institution

Dept. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

2050

Lastpage

2055

Abstract

Fuzzy supervisory predictive control based on genetic algorithm optimization is proposed. For the nonlinear model, through a general objective function dynamically optimized to determine the optimal set-point for a given regulatory level, by using genetic algorithm in order to solve the nonlinear optimization problem for the setpoint, and compared with the supervisory predictive control based on linear model and nonlinear model. Simulation results show the proposed algorithm has better control performance.

Keywords

dynamic programming; fuzzy control; genetic algorithms; nonlinear control systems; nonlinear programming; optimal control; predictive control; dynamic optimization; general objective function; genetic algorithm optimization; nonlinear fuzzy supervisory predictive control; nonlinear model; nonlinear optimization problem; optimal set point determination; Data models; Genetic algorithms; Linear programming; Optimization; Predictive control; Predictive models; fuzzy model; genetic algorithm; nonlinear; supervisory predictive control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852505

Filename

6852505