Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties
This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/658671 |
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| _version_ | 1832561733037719552 |
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| author | Hongyan Yang Huanqing Wang Hamid Reza Karimi |
| author_facet | Hongyan Yang Huanqing Wang Hamid Reza Karimi |
| author_sort | Hongyan Yang |
| collection | DOAJ |
| description | This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme. |
| format | Article |
| id | doaj-art-ccb99248b35e41df9d98fdeb5f70bea9 |
| 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-ccb99248b35e41df9d98fdeb5f70bea92025-02-03T01:24:22ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/658671658671Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic UncertaintiesHongyan Yang0Huanqing Wang1Hamid Reza Karimi2School of Mathematics and Physics, Bohai University, Jinzhou, Liaoning 121000, ChinaSchool of Mathematics and Physics, Bohai University, Jinzhou, Liaoning 121000, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayThis paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.http://dx.doi.org/10.1155/2014/658671 |
| spellingShingle | Hongyan Yang Huanqing Wang Hamid Reza Karimi Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties Abstract and Applied Analysis |
| title | Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties |
| title_full | Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties |
| title_fullStr | Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties |
| title_full_unstemmed | Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties |
| title_short | Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties |
| title_sort | robust adaptive neural backstepping control for a class of nonlinear systems with dynamic uncertainties |
| url | http://dx.doi.org/10.1155/2014/658671 |
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