Erik Wernholt

Nonlinear Identification of a Physically Parameterized Robot Model

In the work presented here, a three-step identification procedure for rigid body dynamics, friction, and flexibilities, introduced in (Wernholt and Gunnarsson, 2005), will be utilized and extended. U ...

Estimation of Nonlinear Effects in Frequency Domain Identification of Industrial Robots

A method for the detection and estimation of nonlinear distortions when identifying multivariable frequency response functions (FRF) is considered. The method is successfully applied to experimental data, which were collected in closed loop, from an industrial robot. The results show that nonlinear distortions are indeed present and cause larger variability in the FRF than the measurement-noise contributions.

Experimental comparison of observers for tool position estimation of industrial robots

This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot.

An extended friction model to capture load and temperature effects in robot joints

Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Available static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed. In this paper, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The effects of these variables are analyzed by means of experiments on a standard industrial robot. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, reducing the average error a factor of 6 when compared to a standard static friction model.

Nonlinear gray-box identification using local models applied to industrial robots

In this paper, we study the problem of estimating unknown parameters in nonlinear gray-box models that may be multivariable, nonlinear, unstable, and resonant at the same time. A straightforward use of time-domain predication-error methods for this type of problem easily ends up in a large and numerically stiff optimization problem. We therefore propose an identification procedure that uses intermediate local models that allow for data compression and a less complex optimization problem. The procedure is based on the estimation of the nonparametric frequency response function (FRF) in a number of operating points. The nonlinear gray-box model is linearized in the same operating points, resulting in parametric FRFs. The optimal parameters are finally obtained by minimizing the discrepancy between the nonparametric and parametric FRFs. The procedure is illustrated by estimating elasticity parameters in a six-axis industrial robot. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest for robot control.

Analysis of methods for multivariable frequency response function estimation in closed loop

Estimation methods for the multivariable frequency response function are analyzed, both in open and closed loop. Expressions for the bias and covariance are derived and the usefulness of these expressions is illustrated in simulations of an industrial robot where the different estimators are compared. The choice of estimator depends on the signal-to- noise ratio as well as the measurement setup and a bias-variance trade-off.

Detection and Estimation of Nonlinear Distortions in Industrial Robots

System identification in robotics often involves the estimation of linear models characterizing the behavior in certain operating points. In this paper, a method for the detection and estimation of nonlinear distortions in an estimated frequency response function (FRF) has successfully been applied to experimental data from an industrial robot. The results show that nonlinear distortions are indeed present and cause larger variability in the FRF than the measurement noise contributions

Frequency-Domain Gray-Box Identification of Industrial Robots

This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, ...

Modeling and Parameter Estimation of Robot Manipulators Using Extended Flexible Joint Models

This paper considers the problem of dynamic modeling and identification of robot manipulators with respect to their elasticities. The so-called flexible joint model, modeling only the torsional gea ...

Experiment design for identification of nonlinear gray-box models with application to industrial robots

Experiment design involving selection of optimal experiment positions for nonlinear gray-box models is studied. From the derived Fisher information matrix, a convex optimization problem is posed. By considering the dual problem, the experiment design is efficiently solved with linear complexity in the number of candidate positions, compared to cubic complexity for the primal problem. In the numerical illustration, using an industrial robot, the parameter covariance is reduced by a factor of six by using the 15 optimal positions compared to using the optimal single position in all experiments.