An identification based indirect iterative learning control via data-driven approach

Author

Ronghu Chi ; Tao Su ; Shangtai Jin

Author_Institution

Sch. of Autom. & Electr. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China

fYear

2012

Firstpage

1773

Lastpage

1776

Abstract

In this paper, an iterative learning control approach is developed for a class of uncertainty nonlinear discrete-time systems based on the identification of the controlled system. At first, the linearized model of the nonlinear system is proposed. And then using the identification method, we present an indirect iterative learning control scheme for the controlled system. Analysis shows that the scheme can guarantee the system convergence under some conditions.

Keywords

adaptive control; convergence of numerical methods; discrete time systems; identification; iterative methods; learning systems; linear systems; linearisation techniques; nonlinear control systems; uncertain systems; controlled system identification; convergence; data-driven approach; identification-based indirect iterative learning control; nonlinear system linearized model; uncertainty nonlinear discrete-time systems; Adaptation models; Control systems; Convergence; Educational institutions; Gold; Nonlinear systems; Robots; convergence; iterative identification; iterative learning control; linearized model; nonlinear discrete system;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4673-1871-6

Electronic_ISBN

978-1-4673-1870-9

Type

conf

DOI

10.1109/ICARCV.2012.6485418

Filename

6485418