Takagi-Sugeno Fuzzy Observer and Extended-Kalman Filter for Adaptive Payload Estimation

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Tarih

2013

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Ieee

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info:eu-repo/semantics/closedAccess

Özet

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.

Açıklama

9th Asian Control Conference (ASCC) -- JUN 23-26, 2013 -- Istanbul, TURKEY

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2013 9Th Asian Control Conference (Ascc)

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