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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Veröffentlicht in:	Asian journal of control Jg. 20; H. 1; S. 115 - 124
Hauptverfasser:	Chi, Ronghu, Hou, Zhongsheng, Jin, Shangtai, Huang, Biao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc 01.01.2018
Schlagworte:	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-Zugang:	Volltext
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