Composite Learning Sliding Mode Control of Flexible-Link Manipulator
This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/9430259 |
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| _version_ | 1832548497389256704 |
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| author | Bin Xu Pengchao Zhang |
| author_facet | Bin Xu Pengchao Zhang |
| author_sort | Bin Xu |
| collection | DOAJ |
| description | This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test. |
| format | Article |
| id | doaj-art-e179018feae44a128626c7c7e973da15 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e179018feae44a128626c7c7e973da152025-02-03T06:13:55ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/94302599430259Composite Learning Sliding Mode Control of Flexible-Link ManipulatorBin Xu0Pengchao Zhang1Shaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaShaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaThis paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.http://dx.doi.org/10.1155/2017/9430259 |
| spellingShingle | Bin Xu Pengchao Zhang Composite Learning Sliding Mode Control of Flexible-Link Manipulator Complexity |
| title | Composite Learning Sliding Mode Control of Flexible-Link Manipulator |
| title_full | Composite Learning Sliding Mode Control of Flexible-Link Manipulator |
| title_fullStr | Composite Learning Sliding Mode Control of Flexible-Link Manipulator |
| title_full_unstemmed | Composite Learning Sliding Mode Control of Flexible-Link Manipulator |
| title_short | Composite Learning Sliding Mode Control of Flexible-Link Manipulator |
| title_sort | composite learning sliding mode control of flexible link manipulator |
| url | http://dx.doi.org/10.1155/2017/9430259 |
| work_keys_str_mv | AT binxu compositelearningslidingmodecontrolofflexiblelinkmanipulator AT pengchaozhang compositelearningslidingmodecontrolofflexiblelinkmanipulator |