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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 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.
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...
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 know that these techniques are not intrinsically robust. An alternative consists in justifying the use of the LS technique. This paper focuses on this issue and introduces a derivation...
Parametric identification requires a good know-how and an accurate analysis. The most popular methods consist in using simply the least squares techniques because of their simplicity. However, these techniques are not intrinsically robust. An alternative consists in helping them with an appropriate data treatment while another choice consists in applying a robust identification method. This paper...
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