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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/984742 |
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| _version_ | 1832555463067041792 |
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| author | Xiaoli Li Kang Wang Yang Li |
| author_facet | Xiaoli Li Kang Wang Yang Li |
| author_sort | Xiaoli Li |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-4e4945b48d9a43569784dd251dd4a75f |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-4e4945b48d9a43569784dd251dd4a75f2025-02-03T05:48:09ZengWileyAbstract 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 |
| spellingShingle | Xiaoli Li Kang Wang Yang Li Multiple Model ILC for Continuous-Time Nonlinear Systems Abstract and Applied Analysis |
| title | 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_short | Multiple Model ILC for Continuous-Time Nonlinear Systems |
| title_sort | multiple model ilc for continuous time nonlinear systems |
| url | http://dx.doi.org/10.1155/2014/984742 |
| work_keys_str_mv | AT xiaolili multiplemodelilcforcontinuoustimenonlinearsystems AT kangwang multiplemodelilcforcontinuoustimenonlinearsystems AT yangli multiplemodelilcforcontinuoustimenonlinearsystems |