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Contribution to Robot System Identification: Noise Reduction using a State Observer

Abstract : Conventional identification approach based on the inverse dynamic identification model using least-squares and direct and inverse dynamic identification techniques has been effectively used to identify inertial and friction parameters of robots. However these methods require a well-tuned filtering of the observation matrix and the measured torque to avoid bias in identification results. Meanwhile, the cutoff frequency of the lowpass filter f c must be well chosen, which is not always easy to do. In this paper, we propose to use a Kalman filter to reduce the noise of the observation matrix and the output torque signal of the PID controller.
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https://hal-uphf.archives-ouvertes.fr/hal-03788153
Contributor : Bilal TOUT Connect in order to contact the contributor
Submitted on : Monday, September 26, 2022 - 11:58:48 AM
Last modification on : Thursday, November 3, 2022 - 11:14:03 AM

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Bilal Tout, Jason Chevrie, Laurent Vermeiren, Antoine Dequidt. Contribution to Robot System Identification: Noise Reduction using a State Observer. 19th International Conference on Informatics in Control, Automation and Robotics, Jul 2022, Lisbon, Portugal. pp.695-702, ⟨10.5220/0011322600003271⟩. ⟨hal-03788153⟩

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