On iterative learning control for MIMO nonlinear systems in the presence of time-iteration-varying parameters

On iterative learning control for MIMO nonlinear systems in the presence of... In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

On iterative learning control for MIMO nonlinear systems in the presence of time-iteration-varying parameters

Loading next page...
 
/lp/springer_journal/on-iterative-learning-control-for-mimo-nonlinear-systems-in-the-2jCMC0T3Nx
Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3604-0
Publisher site
See Article on Publisher Site

Abstract

In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method.

Journal

Nonlinear DynamicsSpringer Journals

Published: Jun 16, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off