Please use this identifier to cite or link to this item: 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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