Takagi-Sugeno fuzzy observer and extended-Kalman filter for adaptive payload estimation
Conference paper > Full Text ยท 2013
Authors: Selami Beyhan, Zsofia Lendek, Musa ALCI, Robert Babuska
2013 9th Asian Control Conference (ASCC), United States of America, International, Invitee, Full Text, 2013-2013, pp 1-6
DOI: 10.1109/ascc.2013.6606241
Summary
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.