Anticipatory iterative learning control for nonlinear systems with arbitrary relative degree

Author

Sun, Mingxuan ; Wang, Danwei

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

46

Issue

5

fYear

2001

fDate

5/1/2001 12:00:00 AM

Firstpage

783

Lastpage

788

Abstract

In this paper, the anticipatory iterative learning control is extended to a class of nonlinear continuous-time systems without restriction on relative degree. The learning algorithm calculates the required input action for the next operation cycle based on the pair of input action taken and its resultant variables. The tracking error convergence performance is examined under input saturation being taken into account. The learning algorithm is shown effective even if differentiation of any order from the tracking error is not used

Keywords

continuous time systems; convergence of numerical methods; integration; intelligent control; learning (artificial intelligence); nonlinear systems; tracking; continuous-time systems; convergence; input saturation; iterative learning control; learning algorithm; nonlinear systems; relative degree; tracking error; Control design; Control systems; Convergence; Error correction; Iterative algorithms; Noise measurement; Nonlinear control systems; Nonlinear systems; Pollution measurement; Sun;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.920801

Filename

920801