A New Closed-Loop Output Error Method for Parameter Identification of Robot Dynamics

Abstract

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

Automated Model Tuning Using A Genetic Algorithm

“…Nonlinear programming algorithms are used to solve a nonlinear least-squares problem in order to converge on the optimal system parameters [4].…”

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

“…Perhaps, more recent and relevant work to this paper has been presented in Gautier et al (2008) with more detailed information and theory including proofs is given in Gautier et al (2013). The work is focused on the identification of robotic manipulator dynamic parameters from torque measurements only.…”

Identification of robot dynamic parameters using Jacobi differentiator

“…Γ f is the friction torque which is usually modelled at non zero velocity as: Γ fj = F sj sign(q j ) + F vjqj + Γ offj , whereq j is the velocity of joint j, sign(x) denotes the sign function. F vj , F sj are the viscous and Coulomb friction coefficients of joint j, Γ offj is an offset parameter which is the dis-symmetry of the Coulomb friction with respect to the sign of the velocity and is due to the current amplifier offset which supplies the motor [26] . Notice that the Coulomb friction contains the non-linear term sign(q j ) which causes discontinuities on Γ f during the crossing of 0 velocityq j .…”