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https://hdl.handle.net/11499/7935
Title: | Takagi-Sugeno fuzzy observer and extended-Kalman filter for adaptive payload estimation | Authors: | Beyhan, Selami Lendek, Z. Alci, M. Babuska, R. |
Keywords: | Better performance Convergence rates Nonlinear state estimation Parameter dependents Real-time experiment Simulation studies System Dynamics Variable payload Kalman filters Servomechanisms Estimation |
Abstract: | In this paper, two nonlinear state estimation methods, Takagi-Sugeno fuzzy observer and extended-Kalman filter are compared in terms of their ability to reliably estimate the velocity and an unknown, variable payload of a nonlinear servo system. Using the system dynamics and a position measurement, the velocity and unknown payload are estimated. In a simulation study, the servo system is excited with a randomly generated step input. In real-time experiments, the estimation is performed under feedback-linearizing control. The performance of the TS fuzzy payload estimator is discussed with respect to the choice of the desired convergence rate. The application results show that the Takagi-Sugeno fuzzy observer provides better performance than the extended-Kalman filter with robust and less parameter dependent structure. © 2013 IEEE. | URI: | https://hdl.handle.net/11499/7935 https://doi.org/10.1109/ASCC.2013.6606241 |
ISBN: | 9781467357692 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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