Multiple Model ILC for Continuous-Time Nonlinear Systems

Multiple Model ILC for Continuous-Time Nonlinear Systems

Multiple model iterative learning control (MMILC) method is proposed to deal with the continuous-time nonlinear system with uncertain and iteration-varying parameters. In this kind of control strategy, multiple models are established to cover the uncertainty of system; a switching mechanism is used...

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Main Authors: Xiaoli Li, Kang Wang, Yang Li
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/984742
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spelling doaj-4e4945b48d9a43569784dd251dd4a75f2020-11-24T22:11:24ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/984742984742Multiple Model ILC for Continuous-Time Nonlinear SystemsXiaoli Li0Kang Wang1Yang Li2School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of International Studies, Communication University of China (CUC), Beijing 100024, ChinaMultiple model iterative learning control (MMILC) method is proposed to deal with the continuous-time nonlinear system with uncertain and iteration-varying parameters. In this kind of control strategy, multiple models are established to cover the uncertainty of system; a switching mechanism is used to decide the most appropriate model for system in current iteration. For system operating iteratively in a fixed time interval with uncertain or jumping parameters, this kind of MMILC can improve the transient response and control property greatly. Asymptotical convergence is demonstrated theoretically, and the control effectiveness is illustrated by numerical simulation.http://dx.doi.org/10.1155/2014/984742
collection DOAJ
language English
format Article
sources DOAJ
author Xiaoli Li
Kang Wang
Yang Li
spellingShingle Xiaoli Li
Kang Wang
Yang Li
Multiple Model ILC for Continuous-Time Nonlinear Systems
Abstract and Applied Analysis
author_facet Xiaoli Li
Kang Wang
Yang Li
author_sort Xiaoli Li
title Multiple Model ILC for Continuous-Time Nonlinear Systems
title_short Multiple Model ILC for Continuous-Time Nonlinear Systems
title_full Multiple Model ILC for Continuous-Time Nonlinear Systems
title_fullStr Multiple Model ILC for Continuous-Time Nonlinear Systems
title_full_unstemmed Multiple Model ILC for Continuous-Time Nonlinear Systems
title_sort multiple model ilc for continuous-time nonlinear systems
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2014-01-01
description Multiple model iterative learning control (MMILC) method is proposed to deal with the continuous-time nonlinear system with uncertain and iteration-varying parameters. In this kind of control strategy, multiple models are established to cover the uncertainty of system; a switching mechanism is used to decide the most appropriate model for system in current iteration. For system operating iteratively in a fixed time interval with uncertain or jumping parameters, this kind of MMILC can improve the transient response and control property greatly. Asymptotical convergence is demonstrated theoretically, and the control effectiveness is illustrated by numerical simulation.
url http://dx.doi.org/10.1155/2014/984742
work_keys_str_mv AT xiaolili multiplemodelilcforcontinuoustimenonlinearsystems
AT kangwang multiplemodelilcforcontinuoustimenonlinearsystems
AT yangli multiplemodelilcforcontinuoustimenonlinearsystems
_version_ 1725805943673847808

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