Intelligent identification of uncertainty bounds for robust servo controlled system
In this paper a new intelligent identification method of uncertainty bound utilizes an adaptive neurofuzzy inference system (ANFIS) in a feedback scheme isnproposed. The proposed ANFIS feedback structurenperforms better in determining the uncertainty bounds withnminimum number of iterations and erro...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/5385/1/iccaie2010_safanah.pdf http://irep.iium.edu.my/5385/ http://dx.doi.org/10.1109/ICCAIE.2010.5735144 |
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| Summary: | In this paper a new intelligent identification method of uncertainty bound utilizes an adaptive neurofuzzy inference system (ANFIS) in a feedback scheme isnproposed. The proposed ANFIS feedback structurenperforms better in determining the uncertainty bounds withnminimum number of iterations and error. In our proposedntechnique, the intelligent identified uncertainty weightingnfunction is validated utilizing v-gap to ensure the stability of
the designed H� controlled system. Our proposed intelligent
identification of uncertainty bound is demonstrated on a
servo motion system. Simulation and experimental results
show that the new ANFIS identifier is more reliable and
highly efficient in estimating the best uncertainty weightingnfunction for robust controller design |
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