Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis
Identification of uncertainty bounds in robust control design is known to be a critical issue that attracts the attention of research in robust control field recently. Nevertheless, the practical implementation involves a trial and error procedure, which depends on the designer prior knowledge and t...
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Inderscience Publishers
2012
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| Online Access: | http://irep.iium.edu.my/28159/ http://irep.iium.edu.my/28159/1/IJMIC150304_RAAFAT.pdf |
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iium-281592017-06-21T06:12:19Z http://irep.iium.edu.my/28159/ Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis Akmeliawati, Rini Raafat, Safanah Martono, Wahyudi TJ212 Control engineering Identification of uncertainty bounds in robust control design is known to be a critical issue that attracts the attention of research in robust control field recently. Nevertheless, the practical implementation involves a trial and error procedure, which depends on the designer prior knowledge and the available information about the system under study. Artificial intelligent techniques provide a suitable solution to such a problem. In this paper a new intelligent identification method of uncertainty bound utilises an adaptive neuro-fuzzy inference system (ANFIS) in an enhanced feedback scheme is proposed. The proposed ANFIS structure enables accurate determination of the uncertainty bounds and guarantees robust stability and performance. In our proposed technique, the validation of the intelligent identified uncertainty weighting function is based on the measurement of both the v-gap metric and the stability margin that result from the corresponding robust controller design. Additionally, these two indices are used to improve the accuracy of the intelligent estimation of uncertainty bound in conjunction with the robust control design requirements. The enhanced intelligent identification of uncertainty bound is demonstrated on a servo positioning system. Simulation and experimental results proves the validity of the applied approach; more reliable and highly efficient estimation of the uncertainty weighting function for robust controller design. Inderscience Publishers 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/28159/1/IJMIC150304_RAAFAT.pdf Akmeliawati, Rini and Raafat, Safanah and Martono, Wahyudi (2012) Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis. International Journal of Modelling, Identification and Control, 15 (3). pp. 173-184. ISSN 1746-6172 E-ISSN 1746-6180 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TJ212 Control engineering |
| spellingShingle |
TJ212 Control engineering Akmeliawati, Rini Raafat, Safanah Martono, Wahyudi Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis |
| description |
Identification of uncertainty bounds in robust control design is known to be a critical issue that attracts the attention of research in robust control field recently. Nevertheless, the practical implementation involves a trial and error procedure, which depends on the designer prior knowledge and the available information about the system under study. Artificial intelligent techniques provide a suitable solution to such a problem. In this paper a new intelligent identification method of uncertainty bound utilises an adaptive neuro-fuzzy inference system (ANFIS) in an enhanced feedback scheme is proposed. The proposed ANFIS structure enables accurate determination of the uncertainty bounds and guarantees robust stability and performance. In our proposed technique, the validation of the intelligent identified uncertainty weighting function is based on the measurement of both the v-gap metric and the stability margin that result from the corresponding robust controller design. Additionally, these two indices are used to improve the accuracy of the intelligent estimation of uncertainty bound in conjunction with the robust control design requirements. The enhanced intelligent identification of
uncertainty bound is demonstrated on a servo positioning system. Simulation and experimental results proves the validity of the applied approach; more reliable and highly efficient estimation of the uncertainty weighting function for robust controller design. |
| format |
Article |
| author |
Akmeliawati, Rini Raafat, Safanah Martono, Wahyudi |
| author_facet |
Akmeliawati, Rini Raafat, Safanah Martono, Wahyudi |
| author_sort |
Akmeliawati, Rini |
| title |
Improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| title_short |
Improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| title_full |
Improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| title_fullStr |
Improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| title_full_unstemmed |
Improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| title_sort |
improved intelligent identification of uncertainty
bounds: design, model validation and stability
analysis |
| publisher |
Inderscience Publishers |
| publishDate |
2012 |
| url |
http://irep.iium.edu.my/28159/ http://irep.iium.edu.my/28159/1/IJMIC150304_RAAFAT.pdf |
| first_indexed |
2023-09-18T20:41:39Z |
| last_indexed |
2023-09-18T20:41:39Z |
| _version_ |
1777409405077159936 |