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Indexed by:期刊论文

Date of Publication:2014-03-01

Journal:JOURNAL OF SUPERCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:67

Issue:3

Page Number:653-670

ISSN No.:0920-8542

Key Words:Iterative learning control; Rank-one update; Nonlinear systems; Quasi-Newton method

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.