Anthony Jubien

Joint stiffness identification from only motor force/torque data

This paper deals with joint stiffness identification with only actual motor force/torque data instead of motor and load positions. The parameters are estimated by using the DIDIM method which needs only input data. This method was previously validated on a 6 DOF rigid robot and is now extended to flexible systems. The criterion to be minimized is the quadratic error between the measured actual motor force/torque and the simulated one. The optimal parameters are calculated with the Nelder - Mead simplex algorithm. An experimental setup exhibits the experimental identification results and shows the effectiveness of our approach.

Dynamic identification of the Kuka LightWeight robot: Comparison between actual and confidential Kuka's parameters

This paper deals with the dynamic identification of the Kuka LightWeight Robot LWR4+. Although this robot is widely used for research purposes by many laboratories, there is not yet a published dynamic model available for model based control or simulation. Because Kuka does not give any information about the dynamic parameters of the robot we propose to identify 2 sets of parameters using the usual off-line identification method which is based on the Inverse Dynamic Identification Model and linear Least Squares technique (IDIM-LS). The first set is obtained by the actual dynamic parameters of links and joints (inertia, gravity and friction parameters) which are identified from motor torques and motor positions data. The second set is obtained by the Kukas inertial parameters of links, implemented in the controller for model-based control. This is a reverse engineering procedure which recovers the confidential manufacturers data. The link parameters are estimated using the IDIM-LS method with the sampled data of the inertia matrix and the gravity torques computed by the controller. To complete the reverse engineering procedure we also identify the joint stiffness parameters used by Kuka to estimate the joint link side position using the joint torque sensors and motor positions data. A Comparison between the actual dynamic parameters and the Kukas parameters allow concluding to a reliable data sheet. This is a strong and very useful result for future work of the scientific community on this very popular robot.

Dynamic Identification of the Kuka LWR Robot Using Motor Torques and Joint Torque Sensors Data

Off-line robot dynamic identification methods use the Inverse Dynamic Identification Model (IDIM), which calculates the motor torques that are linear in relation to the dynamic parameters of both links and drive chains, and use linear least squares technique (IDIM-LS technique). For most robots, the only available data are the motor position and the motor torques which are calculated as the product of the known current reference signal by the joint drive gains. Then the accuracy of links parameters may be limited by noise and error modeling in the drive chains. The Kuka LWR robot (industrial version IIWA: Intelligent Industrial Work Assistant) gives the possibility for an industrial robot to investigate this problem using the joint torque sensors data, measured at the output of the harmonic drive geared drive chains, to identify only the links inertial parameters without the errors coming from the drive chains. This paper focuses on the comparison of the accuracy of the identification of the dynamic parameters of the rigid model of the LWR4+ version, which is very popular in robotics research, using measures of the motor positions and the motor currents, or the torque sensors measurements or both side data. This paper is giving a first complete and reliable identified rigid dynamic model of the LWR4+, publicly available for the robotics community. Moreover, this work shows for the first time the strong result that motor torques calculated from motor currents can identify the links inertial parameters with the same accuracy than using joint torque sensors at the output of the joint drive chains.

Dynamic Identification of flexible joint manipulators with an efficient closed loop output error method based on motor torque output data

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.

A new output error method for a decoupled identification of electrical and mechanical dynamic parameters of DC motor-driven robots

Abstract The identification methods based on the inverse dynamic model allow to use the linear least-squares technique to estimate the dynamic parameters (IDIL-LS method) of mechanical and electrical systems. Assuming a good data filtering to calculate derivatives and reference trajectories that excite the system dynamics, this method has been validated through the experimental identification. Recently, an output error (OE) avoids calculating the derivatives of actual data. This is possible by using the same reference trajectory and the same control law for the actual system and the simulated one. The optimal parameters minimize the 2-norm of the error between the actual output y and the simulated one ys. This nonlinear least-squares problem is dramatically simplified using the inverse dynamic model which is linear to the parameters. This method, called DIDIM (Direct and Inverse Dynamic Identification models), has been validated on robots dynamic parameters identification. In this paper, DIDIM is applied to the decoupled identification of the electrical and mechanical dynamic parameters of DC motor-driven robots using only the reference control voltage of the PWM amplifier and the current reference of the current loop.

Experimental joint stiffness identification depending on measurements availability

This paper addresses the important topic of joint flexibility identification. Three dynamic models depending on measurements availability are compared. The parameters are estimated by using the ordinary least squares of an over linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. An experimental setup exhibits the experimental identification results.

Iterative learning identification and computed torque control of robots

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.

New iterative learning identification and model based control of robots using only actual motor torque data

This paper deals with a new iterative learning dynamic identification method of robot controlled with a Computed Torque Control (CTC) law. The parameters of the Inverse Dynamic Model (IDM) used to compute the CTC, are periodically 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 estimated off-line and the IDIM is calculated with the joint position and its noisy derivatives and cannot take into account on line variations of the parameters (IDIM-LS method). 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. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.

Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots

In this paper, the global identification of spring balancer, dynamic parameters and joint drive gains of a 6 Degrees Of Freedom (DOF) robot is performed. Off-line identification method is based on the use of the Inverse Dynamic Identification Model (IDIM) which takes into account a spring balancer for gravity compensation and linear Least Squares (LS) technique to estimate the parameters from the positions and joint torques. It is key to get accurate values of joint drive gains to get accurate identification because the joint torques are calculated as the product of the current reference by the joint drive gains. Recently a new method validated on small payload robots (less than 10 Kg) allows to identify simultaneously all joint drive gains and dynamic parameters. This method is based on the Total Least Squares (TLS) solution of an over-determined linear system obtained with the inverse dynamic model calculated while the robot is tracking reference trajectories without load and trajectories with a known payload fixed on the robot. This method is used to identify accurately the heavy industrial robot Kuka KR270 (270Kg payload) with its spring balancer. This is a new step to promote a practical and easy to use method for global dynamic identification of any small or heavy gravity compensated industrial robots that does not need any a priori data, which are too often missing from manufacturers data sheet.

New closed-loop output error method for robot joint stiffness identification with motor force/torque data

This paper deals with joint stiffness identification with a new Closed-Loop Output Error (CLOE) method which minimizes the quadratic error between the actual motor force/torque and the simulated one. This method is based on the DIDIM (Direct and Inverse Identification Model) procedure which has been validated on rigid robots and which is now applied to a flexible joint robot. DIDIM method requires a gains updating in the simulated robot in order to keep the bandwidth of the rigid controlled degree of freedom (dof) and to keep the natural frequency of the flexible dof, close to the actual ones, at each step of the recursive Gauss Newton non linear programming algorithm. This gains updating requires a first step of estimating the bandwidth of the rigid controlled dof and the natural frequency of the flexible dof before applying DIDIM method in a second step. An experimental setup exhibits identification results and shows the effectiveness of our approach compared to two classical output error methods.