Monotonic convergence of feedback-aided iterative learning control algorithms in the sense of Lebesgue-p norm
BI Hong-bo
SUN Ming-xuan
CHEN Jia-quan
Abstract：Feedback-aided proportion differentiation (PD)-type iterative learning control is proposed for a class of linear time invari-ant systems in the presence of a fixed initial shift. Based on Lebesgue-p norm, the monotonic convergence analysis result is obtained, and the output trajectory asymptotically convergence to the desired one. Furthermore, a kind of PID-–proportion multiple integration differentiation (PMID)-type learning algorithm with initial rectifying strategy is addressed to realize completely tracking result, and the monotonic convergence analysis processes are stated. Finally, numerical results are presented to demonstrate the tracking performance and the monotonic convergence property of the proposed learning algorithms.
Keywords：iterative learning controlfeedback-aidedinitial rectifyingLebesgue-p normmonotonic convergence
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 965-972 )