Model based nonlinear iterative learning control: A constrained Gauss-Newton approach

Author

Volckaert, M. ; Van Mulders, A. ; Schoukens, J. ; Diehl, M. ; Swevers, J.

Author_Institution

Dept. of Mech. Eng., Katholieke Univ. Leuven, Leuven, Belgium

fYear

2009

fDate

24-26 June 2009

Firstpage

718

Lastpage

723

Abstract

A new method is proposed to solve the model inversion problem that is part of model based iterative learning control (ILC) for nonlinear systems. The model inversion problem consists of finding the input signal corresponding to a given output signal. This problem is formulated as a nonlinear dynamic optimization problem in time domain and solved efficiently using a constrained Gauss-Newton algorithm. A nonlinear ILC algorithm based on this model inversion approach is validated numerically and experimentally. The considered application is an electric circuit described by a polynomial nonlinear state-space model. The nonlinear ILC algorithm shows fast convergence and accurate tracking control.

Keywords

Newton method; adaptive control; inverse problems; iterative methods; learning systems; nonlinear control systems; optimisation; state-space methods; constrained Gauss-Newton approach; model based nonlinear iterative learning control; model inversion problem; nonlinear dynamic optimization; nonlinear systems; polynomial nonlinear state-space model; Circuits; Constraint optimization; Iterative algorithms; Iterative methods; Least squares methods; Newton method; Nonlinear control systems; Nonlinear systems; Polynomials; Recursive estimation; ILC; Learning control systems; nonlinear systems; optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2009. MED '09. 17th Mediterranean Conference on

Conference_Location

Thessaloniki

Print_ISBN

978-1-4244-4684-1

Electronic_ISBN

978-1-4244-4685-8

Type

conf

DOI

10.1109/MED.2009.5164628

Filename

5164628