New Iterative Learning identification and Model Based Control of Robots Using Only Actual Motor Torque Data

Abstract : This paper deals with a new iterative learning dynamic identification method of robot controlled with a Computed Torque Control (CTC) law. The parameters of the Inverse Dynamic Model (IDM), which calculates the CTC, are calculated to minimize the quadratic error between the actual joint force/torque and a joint force/torque calculated with the Inverse Dynamic Identification Model (IDIM), linear in relation to the parameters. Usually the parameters are estimated off-line and the IDIM is calculated with the joint position and the noisy derivative of it and cannot take into account variations of the parameters (IDIM-LS method). The new method called IDIM-ILIC (IDIM with Iterative Learning Identification and Control) overcomes these 2 drawbacks. The parameters are periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.
keyword : identification robot
Type de document :
Communication dans un congrès
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jul 2013, Wollongong, Australia. pp.1436-1441, 2013, <10.1109/AIM.2013.6584296>
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https://hal.archives-ouvertes.fr/hal-00877574
Contributeur : Anthony Jubien <>
Soumis le : lundi 28 octobre 2013 - 19:51:29
Dernière modification le : mercredi 4 janvier 2017 - 16:24:12

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Maxime Gautier, Anthony Jubien, Alexandre Janot. New Iterative Learning identification and Model Based Control of Robots Using Only Actual Motor Torque Data. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jul 2013, Wollongong, Australia. pp.1436-1441, 2013, <10.1109/AIM.2013.6584296>. <hal-00877574>

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