This paper deals with the dynamic parameter identification of the KUKA LBR iiwa R820, a robotic manipulator with flexible joints intended for collaborative applications. The control software interface of this robot provides different signals that allow the parameter identification of not only the real robot, but also of the model integrated in the robot's controller, in a reverse engineering process. By analyzing the successive positions and torques computed by the controller, an accurate set of inertial parameters referring to the robot's model integrated in the controller is obtained. A percent error in the torque reconstruction of less than 1% for every joint, validates the process. Then, this set of parameters is useful to complete and compare the set of parameters that is obtained with real physical measurements which is more prone to errors due to noise and uncertainties. Finally, the torque reconstruction using the measured signals is compared with models present in bibliography, showing a better performance.

Robot identification is a prolific topic that has a long history with results spanning recent decades. Recent years have witnessed a renew of interest in this problem due in part to a rapid increase in robotic hardware platforms capable of accurate model-based control. The most popular methods exploit the fact that the inverse dynamic model is linear to the dynamic parameters. Because we identify robots with closed-loop procedures, an Instrumental Variable approach called IDIM-IV (Inverse Dynamic Identification Model with Instrumental Variable estimation) that combines the direct and inverse dynamic models to prevent from correlation between errors has been successfully validated. However, IDIM-IV does not guarantee that the direct dynamic model will be well-posed during its iterations because of possible modeling errors. In this paper, we combine physical constraints and IDIM-IV to address this deficiency for IDIM-IV. This new constrained IV approach, called PC-IDIM-IV (Physically Consistent IDIM-IV), consists of two nested iterative algorithms: an outer one that is IDIM-IV and an inner one that accounts for the physical constraints solved by a Gauss-Newton algorithm. Experimental results and comparisons with other methods carried out with the TX40 robot show the feasibility of PC-IDIM-IV.

This paper deals with joint stiffness off-line identification with new closed loop output error method which minimizes the quadratic error between the actual motor force/torque and the simulated one. The measurement of the joint position and its derivatives are not necessary. This method called DIDIM (Direct and Inverse Dynamic Identification Models) was previously validated on rigid robots and is now extended to a flexible joint manipulator. DIDIM method for flexible joint manipulators is derived into a three-step procedure: first, a rigid low frequency dynamic model is identified with DIDIM method; second, approximate values of the inertia ratio and stiffness are identified using the total inertia and friction values of step 1 and classical non linear programming algorithm; third, all the dynamic parameters (inertia, friction, stiffness) of the flexible robot are more accurately identified all together, starting from the values identified in step 1 and 2 and using the DIDIM method. An experimental setup exhibits results and shows the effectiveness of our approach compared with a classical output error methods.

This paper deals with the topic of industrial robots identification. The usual identification method is based on the use of the inverse dynamic model (IDM) and least squares (LS) technique. Good results can be obtained provided that a well-tuned bandpass filtering is used. However, we are always in doubt if regressors are exogenous i.e. statistically uncorrelated with error terms. Surprisingly, in papers dealing with identification of real-world systems, exogeneity assumption is never verified whereas it is a fundamental condition to obtain unbiased estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a theoretical method for investigating whether regressors are exogenous or not. The DWH-test makes of the Two Stage Lesat Squares estimator (2SLS) and an augmented LS regression. However, this test cannot be used as is for robots identification: instruments set is supposed to be valid and restrictive statistical assumptions are made while they are quite implausible in practice. In this paper, we aim at bridging the gap between Econometrics and Control engineering practices by introducing a revisited version relevant for robots identification. An experimental validation performed on a 2 degrees of freedom (DOF) robot shows the effectiveness and the usefulness of this revisited DWH-test.

This paper deals with a new iterative learning dynamic identification and control method of robot. The robot is closed-loop controlled with a Computed Torque Control (CTC). The parameters of the Inverse Dynamic Model (IDM), which calculates the CTC are calculated to minimize the quadratic error between the actual joint force/torque and a joint force/torque calculated with the Inverse Dynamic Identification Model (IDIM), linear in relation to the parameters. Usually the parameters are off-line linear least squares estimated (IDIM-LS) where the IDIM is calculated with the joint position and its noisy derivatives, which cannot take into account variations of the parameters. The new method called IDIM-ILIC (IDIM with Iterative Learning Identification and Control) overcomes these 2 drawbacks. The parameters are periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. A study of convergence of the method is performed in simulation and an experimental setup with stationary parameters and with a variation of the payload on a prismatic joint validates the procedure.

Off-line 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 calculated with torque and position sampled data while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. 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. A new method called DIDIM (Direct and Inverse Dynamic Identification Models) has been proposed and validated on a 2 degree-of freedom robot [1]. DIDIM method requires only the joint force/torque measurement. 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. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots.

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking ldquoexcitingrdquo trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy observation matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.

Usually, the identification of the dynamic parameters of robot makes use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories. This allows using linear least squares (LS) techniques to estimate the parameters. The efficiency of this method has been proved through experimental identifications of a lot of prototypes and industrial robots. However, it is known that LS estimators are sensitive to outliers and leverage points. Thus, it may be helpful to verify their reliability. This is possible by using robust regressions and residual analysis. Then, we compare the results with those obtained with classical LS regression. This paper deals with this issue and introduces the experimental identification and residual analysis of an one degree of freedom (DOF) haptic interface using the Huber's estimator. To verify the pertinence of our analyses, this comparison is also performed on a medical interface consisting of a complex mechanical structure.

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking trajectories which excite the system dynamics in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of many prototype and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. The new method bypasses the need to measure or estimate joint position, velocity and acceleration by using both Direct and Inverse Dynamic Identification Models (DIDIM). It needs only torque data at a low sample rate. It is based on a closed loop simulation which integrates the direct dynamic model. The optimal parameters minimize the 2 norm of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem is dramatically simplified using the inverse model to calculate the derivatives of the cost function.

The aim of haptic interfaces is to enhance the user's immersion in virtual environments through the stimulation of the haptic sense (motion capture and force feedback). Most devices make use of an articulated mechanical structure introducing distortions between the operator and the explored world. To assess the quality of the interface, this distortion must be identified. This paper deals with this issue and introduces the modeling and the identification of a 3 degrees of freedom haptic interface using inverse model and least squares method used in robotics.

The haptic interfaces aim at the user's immersion in virtual environments through the stimulation of the haptic sense. Most devices consist of an articulated mechanical structure introducing distortions between the operator and the explored world. This distortion must be identified in order to assess the quality of the interface. The least-squares (LS) regressions are often used because of their simplicity. However, if the identification process takes place when the device is controlled by feedback, then the LS estimator is biased. To verify the reliability of the results, the bias of the estimator must be evaluated. This paper deals with this issue and introduces the design and the application of a derivate of the CESTAC method.

Parametric identification consists in estimating the values of physical parameters of robotic systems. The most popular methods consist in using the least squares regression because of their simplicity. However, we don't know how much they are dependent on the measurement accuracy and so on we ignore the necessary resolution they require to produce good quality results. This paper focuses on this issue and introduces a derivation of the CESTAC method, which will be applied to an industrial 6 degrees of freedom (DOF) serial robot, to estimate the minimal resolution indispensable for an accurate parametric identification.