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Sensorless torque estimation using adaptive Kalman filter and disturbance estimator

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2 Author(s)
Sang-Chul Lee ; Distributed Control and Autonomous Systems Laboratory, Department of mechatronics, Gwangju Institute of Science and Technology (GIST), Korea ; Hyo-Sung Ahn

This paper presents a stochastic estimation method and a signal processing based method for estimating disturbance torques without using any force sensors. The first method will address a robustness against measurement noises by estimating noise covariance. The second method will show several practical merits. By containing system models inside of the estimator, the total disturbance torque injected into the plant is estimated. The experimental results conducted using a master-slave manipulator show the validity of two proposed methods.

Published in:

Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on

Date of Conference:

15-17 July 2010