Frequency-Domain Gray-Box Identification of Industrial Robots
2007 (English)Report (Other academic)
This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, nonlinear, resonant, and unstable system. Unknown parameters (mainly spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. The nonlinear parametric robot model is linearized in the same positions and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model). In order to accurately estimate the nonparametric FRFs, the experiments must be carefully designed. The selection of optimal robot configurations for the experiments is also part of the design. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. , 21 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2826
System identification, Multivariable systems, Nonlinear systems, Closed-loop identification, Frequency response methods, industrial robots
IdentifiersURN: urn:nbn:se:liu:diva-56148ISRN: LiTH-ISY-R-2826OAI: oai:DiVA.org:liu-56148DiVA: diva2:316952