Intelligent identification of uncertainty bounds for robust servo controlled system

• Main Authors: M. Raafat, Safanah, Akmeliawati, Rini, Martono, Wahyudi
• Format: Conference or Workshop Item
• Language: English
• Published: 2010
• Subjects: TJ212 Control engineering
• 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

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