Offline robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method called DIDIM requires only the joint force/torque measurement, which avoids the calculation of the velocity and acceleration by bandpass filtering of the measured position. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a nonlinear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a two degree-of-freedom direct drive rigid robot shows that the new method is efficient.

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.

Synthetic population is used in many transport models ranging from trip-based, hybrid trip, tour-based, and activity-based models. As mobility decisions depend on both individuals’ characteristics and family situation, generating a two-layered population that takes into account not only the individual level but also household level is essential. In the literature, three main categories of methods for two-layered population generation have been proposed. These categories are synthetic reconstruction (SR), combinatorial optimization (CO), and statistical learning (SL). SR and CO methods produce synthetic populations by means of replicating individuals, whereas SL methods generate a population following a joint probability estimation. However, selecting a generation process is not straightforward as it depends on input data and synthetic population characteristics. To the best of our knowledge, no clear methodology for selecting between these methods exists. The main objectives of this paper are to provide (1) a detailed description of the available methods, (2) a comparison between these methods, and (3) a decision-making procedure for selecting between these methods. The description and comparison of the methods relies on different criteria: marginals availability, sample size, number of potential attributes that can be handled, population size to generate, possibility of zero-cell problem, and so forth. The advantages and shortcomings of each method are illustrated, and method performance is assessed. The decision-making procedure is carried out through the proposal of a decision tree. Researchers and practitioners have now access to a comprehensive and unified framework to select the appropriate method depending on available data and features of their modeling purposes.

This brief deals with the identification of industrial robots. The usual identification method is based on the use of the inverse dynamic identification model and the least-squares technique. Another approach is the direct and inverse dynamic identification models (DIDIM) method, which is a closed-loop output error (CLOE) method minimizing the quadratic error between the actual and simulated joint torques. In this brief, the DIDIM method is compared with the usual CLOE method, which minimizes the quadratic error between the actual and simulated joint positions. The comparison is carried out on six degrees of freedom industrial robot. The experimental results show that the DIDIM method needs three iterations to identify 60 parameters while the usual CLOE method needs 20 iterations.