Non-parametric estimates of frequency response functions (FRFs) are often suitable for describing the dynamics of a mechanical system. If treating these estimates as measurements, they can be used for parametric identification of, e.g., a gray-box model. This paper shows that a more accurate parametric model can be identified based on local parametric FRF estimates, giving a shorter total experiment time, compared to classical methods. Classical methods for non-parametric FRF estimation of MIMO (Multiple Input Multiple Output) systems require at least as many experiments as the system has inputs. Local parametric FRF estimation methods have been developed for avoiding multiple experiments. In this paper, these local methods are adapted and applied for estimating the FRFs of a 6-axes robotic manipulator, which is a nonlinear MIMO system operating in closed loop. The aim is to reduce the experiment time and amount of data needed for identification. The resulting FRFs are analyzed in an experimental study and compared to estimates obtained by classical MIMO techniques.

The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.

Strain wave gears are frequently used in small and medium size industrial robots. In order to describe and quantify friction effects in gearboxes of such type, a structurally simple, yet powerful model is proposed taking into account both speed-and load-dependent friction effects. Moreover, position-dependent disturbances in a robotic joint are considered. An identification procedure is presented that allows to separate the individual components of the model and identify them subsequently. The effectiveness of the model and identification procedure is validated using experimental data gathered from four different robotic joints of varying size. Furthermore, the benefits of improved friction modeling are shown by means of different applications, including smooth lead-through programming and sensorless force control.

This work investigates feedforward control of elastic robot structures. A general serial link elastic robot model which can describe a modern industrial robot in a realistic way is presented. The feedforward control problem is discussed and a solution method for the inverse dynamics problem is proposed. This method involves solving a differential algebraic equation (DAE). A simulation example for an elastic two axis planar robot is also included and shows promising results.