A new kind of nonlinear model predictive iterative learning control

Author

Wang, Jinyi ; Liu, Xiangjie

Author_Institution

State Key Lab. of Alternate Electr. Power Syst. with Renewable Energy Sources, North China Electr. Power Univ., Beijing, China

fYear

2012

fDate

23-25 May 2012

Firstpage

1422

Lastpage

1426

Abstract

A nonlinear model predictive controller based on iterative learning control (NMPILC) is proposed. The nonlinear plant dynamic is described by a fuzzy model which contains local liner models. Based on this model, model predictive control algorithm that utilizes past data along with real-time measurements is devised. This algorithm is developed to address the learning rate for a class of repetitive system with non-repetitive disturbances. The iterative learning control law is given. It is shown that the control performance of the proposed NMPILC can be greatly improved by using this on-linear model predictive iterative learning control algorithm.

Keywords

fuzzy set theory; iterative methods; learning systems; nonlinear control systems; predictive control; NMPILC; fuzzy model; iterative learning control law; learning rate; local liner models; nonlinear model predictive controller; nonlinear plant dynamic; nonrepetitive disturbances; real-time measurements; repetitive system; Convergence; Covariance matrix; Mathematical model; Prediction algorithms; Predictive control; Predictive models; Trajectory; Fuzzy model; Iterative Learning Control (ILC); Model Predictive Control (MPC);

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244228

Filename

6244228