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This paper deals with two robot identification methods recently introduced. The first one is based on the use of the Inverse Dynamic Identification Model (IDIM) and the Instrumental Variable (IV). The second one is the Direct and Inverse Dynamic Identification Models (DIDIM) method, which is a closed-loop output error method minimizing the quadratic error between the actual and simulated joint torques...
Identification of industrial robots is a prolific topic that has been deeply investigated over the last three decades. The standard method is based on the use of the inverse dynamic model and the least-squares estimation (IDIM-LS method) while robots are operating in closed loop by tracking exciting trajectories. Recently, in order to secure the consistency of the parameters estimates, an instrumental...
This paper deals with a new CLIE (Closed Loop Input Error) method for dynamic identification of flexible joint robots. This is a straightforward extension of the DIDIM (Direct and Inverse Dynamic Identification Models) method from rigid robots to flexible joint robots. DIDIM is a fast closed loop input error method which minimizes the quadratic error between the actual motor force/torque (the control...
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...
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...
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),...
In many cases of new actuation of compliant controlled or bio-inspired joint driven robot, a global identification of electrical and mechanical coupled dynamics is required. This paper proposes a technique which mixes a closed loop output error method with the inverse dynamic identification model method which allows using linear least-squares technique to estimate the parameters. A first approach...
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...
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...
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...
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...
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.
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...
Friction modeling is essential for joint dynamic identification and control. Joint friction is composed of a viscous and a dry friction force. According to Coulomb law, dry friction depends linearly on the load in the transmission. However, in robotics field, a constant dry friction is frequently used to simplify modeling, identification and control. That is not accurate enough for joints with large...
Usually, the joint transmission friction model for robots is composed of a viscous friction force and of a constant dry friction force. However, according to the Coulomb law, the dry friction force depends linearly on the load driven by the transmission. It follows that this effect must be taken into account for robots working with large variation of the payload or inertial and gravity forces, and...
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...
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...
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...
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...
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...
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