رویکرد متغیر ابزاری برای شناسایی روبات های صنعتی سفت و سخت

This paper deals with the important topic of rigid industrial robots identification. The usual identification method is based on the use of the inverse dynamic model and the least-squares technique. In order to obtain good results, a well-tuned derivative bandpass filtering of joint positions is needed to calculate the joint velocities and accelerations. However, we can doubt whether the bandpass filter is well-tuned or not. Another approach is the instrumental variable (IV) method which is robust to data filtering and which is statistically optimal. In this paper, an IV approach relevant for identification of rigid industrial robots is introduced. The set of instruments is the inverse dynamic model built from simulated data which are calculated from the simulation of the direct dynamic model. The simulation assumes the same reference trajectories and the same control structure for both the actual and the simulated robot and is based on the previous IV estimates. Furthermore, to obtain a rapid convergence, the gains of the simulated controller are updated according to IV estimates. Thus, the proposed approach validates the inverse and direct dynamic models simultaneously and is not sensitive to initial conditions. The experimental results obtained with a 2 degrees of freedom (DOF) planar prototype and with a 6 DOF industrial robot show the effectiveness of our approach: it is possible to identify 60 parameters in 3 iterations and in 11 s.

The usual robots identification method is based on the use of the inverse dynamic model (IDM) and the least-squares technique (LS). This method, called as Inverse Dynamic Identification Model with Least Squares technique (IDIM-LS) has been validated on several industrial robots, see Khalil and Dombre (2002), Swevers, Verdonck, and De Schutter (2007), Hollerbach, Khalil, and Gautier (2008), and Gautier, Janot, and Vandanjon (2012) and the references given therein. In order to obtain good results, a well-tuned derivative bandpass filtering of joint positions is needed to calculate the joint velocities and accelerations. However, even with the guidelines given in Gautier et al. (2012) to tune the bandpass filter, the users can doubt whether the IDIM-LS estimates are unbiased or not.

In this paper, an instrumental variable approach relevant for offline identification of rigid industrial robots is introduced. This method is called as IDIM-IV method. The set of instruments is the inverse dynamic model built from simulated data calculated from the simulation of the direct dynamic model of the robot. The simulation assumes the same reference trajectories, the same control structure for both the actual and the simulated robot and is based on previous IDIM-IV estimates. In order to have a rapid convergence, the gains of the simulated controller are updated according to IDIM-IV estimates.