An average operator-based PD-type iterative learning control for variable initial state error

Author

Park, Kwang-Hyun

Author_Institution

Human-Friendly Welfare Robot Syst. Eng. Res. Center, Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea

Volume

50

Issue

6

fYear

2005

fDate

6/1/2005 12:00:00 AM

Firstpage

865

Lastpage

869

Abstract

This note studies the effect of variable initial state error in iterative learning control (ILC) systems and proposes a new ILC algorithm based on an average operator. Then, it is shown that, when the proposed algorithm is applied to linear time-invariant (LTI) systems, the effect of the initial state error can be exactly estimated under a specific condition, while the existing algorithms guarantee only the boundness of the error or the convergence from stochastic point of view. To show the validity of the proposed algorithm, a numerical example is given.

Keywords

PD control; adaptive control; errors; iterative methods; learning systems; linear systems; average operator-based PD-type iterative learning control; error boundness; linear time-invariant systems; variable initial state error; Control systems; Convergence; Error correction; Iterative algorithms; Iterative methods; Nonlinear systems; Service robots; State estimation; Stochastic systems; Sun; Average operator; iterative learning control; variable initial state error;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2005.849249

Filename

1440574