Name
Abstract | ||
|---|---|---|
This paper deals with the important topic of industrial robot identification. The usual identification method is based on the inverse dynamic identification model and the least squares technique. This method has been successfully applied on several industrial robots. Good results can be obtained, provided a well tuned derivative band-pass filtering of joint positions is used to calculate the joint velocities and accelerations. However, one cannot be sure whether or not the band-pass filtering is well tuned. An alternative is the instrumental variable (IV) method, which is robust to data filtering and is statistically optimal. In this paper, a generic IV approach suitable for robot identification is proposed. The instrument set is the inverse dynamic model built from simulated data calculated from simulation of the direct dynamic model. The simulation is based on previous estimates and assumes the same reference trajectories and the same control structure for both actual and simulated robots. Finally, gains of the simulated controller are updated according to IV estimates to obtain a valid instrument set at each step of the algorithm. The proposed approach validates the inverse and direct dynamic models simultaneously, is not sensitive to initial conditions, and converges rapidly. Experimental results obtained on a six-degrees-of-freedom industrial robot show the effectiveness of this approach: 60 dynamic parameters are identified in three iterations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCST.2013.2246163 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
band-pass filters,identification,industrial robots,iterative methods,least squares approximations,robot dynamics,statistical analysis,data filtering,derivative band-pass filtering,direct dynamic model,generic IV approach,generic instrumental variable approach,industrial robot identification,inverse dynamic identification model,inverse dynamic model,joint accelerations,joint positions,joint velocities,least squares technique,six-degrees-of-freedom industrial robot,Closed-loop identification,instrumental variable method,model reduction,rigid robot dynamics,statistical hypotheses testing | Least squares,Control theory,Control theory,Iterative method,Instrumental variable,Filter (signal processing),Control engineering,Automation,Industrial robot,Robot,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 1 | 1063-6536 |
Citations | PageRank | References |
17 | 1.00 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre Janot | 1 | 86 | 12.37 |
| P. O. Vandanjon | 2 | 26 | 3.23 |
| Maxime Gautier | 3 | 477 | 76.28 |