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Journal of Control Science and Engineering
Volume 2017 (2017), Article ID 1731676, 6 pages
Research Article

An Accelerating Iterative Learning Control Based on an Adjustable Learning Interval

School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, China

Correspondence should be addressed to Dongqi Ma; moc.361@9940iqgnodam

Received 8 December 2016; Accepted 13 February 2017; Published 2 March 2017

Academic Editor: William MacKunis

Copyright © 2017 Dongqi Ma and Hui Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An iterative learning control algorithm with an adjustable interval is proposed for nonlinear systems to accelerate the convergence rate of iterative learning control. For -norm, the monotonic convergence of ILC was analyzed, and the corresponding convergence conditions were obtained. The results showed that the convergence rate was mainly determined by the controlled object, the control law gain, the correction factor, and the iteration interval size and that the control law gain was corrected in real time in the modified interval and the modified interval shortened as the number of iterations increased, further accelerating the convergence. The numerical simulation shows the effectiveness of the proposed method.