Title :
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
fDate :
May 31 2014-June 2 2014
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;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852505
