Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2014-03-01

Journal: JOURNAL OF SUPERCOMPUTING

Included Journals: Scopus、EI、SCIE

Volume: 67

Issue: 3

Page Number: 653-670

ISSN: 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.