Output Error Methods for Robot Identification

Mathieu Brunot (1) , Alexandre Janot (1) , Francisco Carrillo (2) , Joono Cheong (3) , Jean-Philippe Noël (4)

1 DTIS, ONERA, Université de Toulouse [Toulouse]
2 ENIT - Ecole Nationale d'Ingénieurs de Tarbes
3 POSTECH - Pohang University of Science and Technology
4 Laboratoire de Structures et Systèmes Spatiaux

Industrial robot identification is usually based on the Inverse Dynamic Identification Model (IDIM) that comes from the Newton's laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the Least-Squares (LS) or the Instrumental Variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model’s parameters are iteratively tuned in order to match the simulated model’s output and the measured system’s output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a 6-degree-of-freedom rigid robot.

Mots clés: IDENTIFICATION PARAMETRIQUE; ROBOTIQUE

Domaines: Sciences de l'ingénieur [physics]; Physique [physics]; Mathématiques [math]; Informatique [cs]

Mathieu Brunot, Alexandre Janot, Francisco Carrillo, Joono Cheong, Jean-Philippe Noël. Output Error Methods for Robot Identification. Journal of Dynamic Systems, Measurement, and Control, 2019, 142 (3), pp.031002-1 - 031002-9. ⟨10.1115/1.4045430⟩. ⟨hal-02459180⟩