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New quasi-Newton iterative learning control scheme based on rank-one update for nonlinear systems.

Authors :
Xu, Guangwei
Shao, Cheng
Han, Yu
Yim, Kangbin
Source :
Journal of Supercomputing; Mar2014, Vol. 67 Issue 3, p653-670, 18p
Publication Year :
2014

Abstract

This paper develops an algorithm for iterative learning control on the basis of the quasi-Newton method for nonlinear systems. The new quasi-Newton iterative learning control scheme using the rank-one update to derive the recurrent formula has numerous benefits, which include the approximate treatment for the inverse of the system's Jacobian matrix. The rank-one update-based ILC also has the advantage of extension for convergence domain and hence guaranteeing the choice of initial value. The algorithm is expressed as a very general norm optimization problem in a Banach space and, in principle, can be used for both continuous and discrete time systems. Furthermore, a detailed convergence analysis is given, and it guarantees theoretically that the proposed algorithm converges at a superlinear rate. Initial conditions which the algorithm requires are also established. The simulations illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09208542
Volume :
67
Issue :
3
Database :
Complementary Index
Journal :
Journal of Supercomputing
Publication Type :
Academic Journal
Accession number :
94629515
Full Text :
https://doi.org/10.1007/s11227-013-0960-5