A revised Durbin-Wu-Hausman test for industrial robot identification

Alexandre Janot (1) , Pierre Olivier Vandanjon (2) , Maxime Gautier (3)

1 ONERA - The French Aerospace Lab [Toulouse]
2 IFSTTAR/AME/EASE - Environnement, Aménagement, Sécurité et Eco-conception
3 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

This paper addresses the topic of robot identification. The usual identification method makes use of the inverse dynamic model (IDM) and the least squares (LS) technique while robot is tracking exciting tra- jectories. Assuming an appropriate bandpass filtering, good results can be obtained. However, the users are in doubt whether the columns of the observation matrix (the regressors) are uncorrelated (exo- genous) or correlated (endogenous) with the error terms. The exogeneity condition is rarely verified in a formal way whereas it is a fundamental condition to obtain unbiased LS estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a formal statistic for investigating whether the regressors are exogenous or endogenous. However, the DWH-test cannot be straightforwardly used for robot identifi- cation because it is assumed that the set of instruments is valid. In this paper, a Revised DWH-test suitable for robot identification is proposed. The revised DWH-test validates/invalidates the instruments chosen by the user and validates the exogeneity assumption through the calculation of the QR factor- ization of the augmented observation matrix combined with a F-test if required. The experimental results obtained with a 6 degrees-of-freedom (DOF) industrial robot validate the proposed statistic.

Mots clés: ROBOT; IDENTIFICATION; DYNAMIQUE; METHODE; VARIABLE; STATISTIQUE

Domaines: Automatique / Robotique

Alexandre Janot, Pierre Olivier Vandanjon, Maxime Gautier. A revised Durbin-Wu-Hausman test for industrial robot identification. Control Engineering Practice, 2016, 48, pp.52-62. ⟨10.1016/j.conengprac.2015.12.017⟩. ⟨hal-01467446⟩