Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control

Computational complexity and model dependence are two significant limitations on lifted norm optimal iterative learning control (NOILC). To overcome these two issues and retain monotonic convergence in iteration, this paper proposes a computationally‐efficient non‐lifted NOILC strategy for nonlinear...

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Bibliographic Details

Published in:	Asian journal of control Vol. 20; no. 1; pp. 115 - 124
Main Authors:	Chi, Ronghu, Hou, Zhongsheng, Jin, Shangtai, Huang, Biao
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc 01.01.2018
Subjects:	Chemical reactions Complexity Computationally‐light algorithm Computer simulation Data‐driven control approach Discrete time systems Iterative methods Learning Nonlinear control Nonlinear discrete‐time systems Nonlinear systems Norm optimal ILC
ISSN:	1561-8625, 1934-6093
Online Access:	Get full text
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