15th Triennial World Congress of the International Federation of Automatic Control
  Barcelona, 21–26 July 2002 
ON THE MONOTONIC CONVERGENCE OF HIGH ORDER ITERATIVE LEARNING UPDATE LAWS
Kevin L. Moore* and YangQuan Chen*,1
* Center for Self-Organizing and Intelligent Systems (CSOIS)
Dept. of Electrical and Computer Engineering
UMC 4160, College of Engineering, 4160 Old Main Hill
Utah State University, Logan, UT 84322-4160, USA
High-order iterative learning control (ILC) in both iteration domain and time domain is investigated in this paper. We are interested in whether a high order iterative learning updating law is helpful in achieving a monotonic convergence in a suitable norm topology other than the exponentially weighted sup-norm. Discrete-time linear time invariant system is considered. With simulation illustrations, it is shown that a high-order scheme in both time domain and iteration domain is helpful. A new design framework for high order ILC is proposed.
Keywords: iterative learning control; high order updating law; time domain; iteration domain; small gain theorem; internal model principle
1Corresponding Author: Dr YangQuan Chen. Tel. 01-435-7970148; Fax: 01-435-7972003. URL: http://www.crosswinds.net/~yqchen.

E-mail: yqchen@ieee.org
Session slot T-Th-A16: Higher-Order Iterative Learning Control/Area code 2a : Control Design