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Machine Intelligence Research
Erschienen in: International Journal of Automation and Computing 3/2015

01.06.2015 | Regular Paper

A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

Erschienen in: Machine Intelligence Research | Ausgabe 3/2015

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Abstract

In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iteration-varying desired trajectories. A high-order internal model (HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors (PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.
Vorheriger Artikel Distributed adaptive synchronization of complex dynamical network with unknown time-varying weights
Nächster Artikel Genetic algorithm with variable length chromosomes for network intrusion detection

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Metadaten
Titel
A high-order internal model based iterative learning control scheme for discrete linear time-varying systems
Publikationsdatum
01.06.2015
Erschienen in
Machine Intelligence Research / Ausgabe 3/2015
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI
https://doi.org/10.1007/s11633-015-0886-x

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