Iterative Learning Identification and Computed Torque Control of Robots

Abstract : This paper deals with a new iterative learning dynamic identification and control method of robot. The robot is closed-loop controlled with a Computed Torque Control (CTC). 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. 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. A study of convergence of the method is performed in simulation and an experimental setup with stationary parameters and with a variation of the payload on a prismatic joint validates the procedure.
keyword : identification robot
Type de document :
Communication dans un congrès
IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), Nov 2013, Tokyo, Japan. pp.3419-3424, 2013, <10.1109/IROS.2013.6696843>
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https://hal.archives-ouvertes.fr/hal-00877571
Contributeur : Anthony Jubien <>
Soumis le : lundi 28 octobre 2013 - 19:43:05
Dernière modification le : mercredi 4 janvier 2017 - 16:24:12

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Maxime Gautier, Anthony Jubien, Alexandre Janot. Iterative Learning Identification and Computed Torque Control of Robots. IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), Nov 2013, Tokyo, Japan. pp.3419-3424, 2013, <10.1109/IROS.2013.6696843>. <hal-00877571>

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