Projection algorithm based adaptive iterative learning control for a class of discrete-time systems

Author

Baobin Liu ; Wei Zhou

Author_Institution

Coll. of Eng., Jiangsu Inst. of Commerce, Nanjing, China

fYear

2015

fDate

23-25 May 2015

Firstpage

2993

Lastpage

2998

Abstract

In this paper, adaptive iterative learning control scheme is designed for a class of discrete-time uncertain systems with random initial state and unknown control gain. The system uncertainty is generated by a stable high-order internal model. The proposed controller incorporates a projection algorithm. Through rigorous analysis, the asymptotical learning convergence along the iteration axis in a finite time interval can be guaranteed, provided the desired trajectory is iteration-varying.

Keywords

adaptive control; control system synthesis; discrete time systems; iterative learning control; learning systems; stability; uncertain systems; asymptotical learning convergence; discrete-time uncertain systems; iteration-varying trajectory; projection algorithm based adaptive iterative learning control design; random initial state; stable high-order internal model; system uncertainty; unknown control gain; Adaptive systems; Convergence; Discrete-time systems; Projection algorithms; Trajectory; Uncertainty; Adaptive Iterative Learning Control; High-Order Internal Model and Random Initial Condition; Time-Iteration-Varying Parametric Uncertainty; Unknown Control Gain;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162434

Filename

7162434