A new closed-loop output error method for parameter identification of robot dynamics

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 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 requires only the joint force/torque measurement. 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 non-linear 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 2 degree-of-freedom direct drive robot shows that the new method is efficient

Identification of Robot Dynamics: A Parallel Processing Approach

Knowledge of the exact characteristics of robot manipulators is one of the most significant factors in designing motion control systems, since the control performance is directly dependant upon the accuracy of the dynamic model. Dynamic models normally have complicated behaviour, including varying inertia depending upon the arm configuration, uncertain load effects, non-linear effects such as the Coriolis and Centripetal forces and interactions among joints. Unless these characteristics are included in the manipulator dynamics exactly, the performance of the controller is not expected to meet the given requirements. This necessitates the development of an efficient method to identify the dynamic parameters of robot arms. This paper will describe the on-line estimation of the link inertial parameters using a semi-customised symbolic representation of the dynamic equations based on the Lagrangian formulation.......

Robot Excitation Trajectories for Dynamic Parameter Estimation using Optimized B-Splines

In this paper we adressed the problem of finding exciting trajectories for the identification of manipulator link inertia parameters. This can be formulated as a constraint nonlinear optimization problem. The new approach in the presented method is the parameterization of the trajectories with optimized B-splines. Experiments are carried out on a 7 joint Light-Weight robot with torque sensoring in each joint. Thus, unmodeled joint friction and noisy motor current measurements must not be taken into account. The estimated dynamic model is verified on a different validation trajectory. The results show a clear improvement of the estimated dynamic model compared to a CAD-valued model

Identification dynamique de robots avec un modèle de frottement sec fonction de la charge et de la vitesse

International audienceEn robotique, les pertes dans la chaine d'actionnement articulaire des robots sont généralement prises en compte dans le modèle dynamique par un effort de frottement visqueux proportionnel à la vitesse et par un effort constant de frottement sec. Pourtant, d'après la loi de Coulomb, le frottement sec de glissement varie avec les efforts de contact dans les éléments de transmission. Ainsi, cet effet est à prendre en compte pour les systèmes mécaniques soumis à de fortes variations de charge. Cet article présente un nouveau modèle dynamique dans lequel l'effort de frottement sec est proportionnel à la charge selon un coefficient dépendant de la vitesse. Une nouvelle procédure permet d'identifier ce modèle à partir de mesures faites sur le robot réalisant diverses trajectoires avec différents cas de charge. Une validation expérimentale est réalisée sur un robot industriel

Identification dynamique de robots avec un modèle de frottement sec fonction de la charge et de la vitesse

International audienceEn robotique, les pertes dans la chaine d'actionnement articulaire des robots sont généralement prises en compte dans le modèle dynamique par un effort de frottement visqueux proportionnel à la vitesse et par un effort constant de frottement sec. Pourtant, d'après la loi de Coulomb, le frottement sec de glissement varie avec les efforts de contact dans les éléments de transmission. Ainsi, cet effet est à prendre en compte pour les systèmes mécaniques soumis à de fortes variations de charge. Cet article présente un nouveau modèle dynamique dans lequel l'effort de frottement sec est proportionnel à la charge selon un coefficient dépendant de la vitesse. Une nouvelle procédure permet d'identifier ce modèle à partir de mesures faites sur le robot réalisant diverses trajectoires avec différents cas de charge. Une validation expérimentale est réalisée sur un robot industriel

Dynamic Modeling and Identification of Joint Drive with Load-Dependent Friction Model

International audienceFriction 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 payload or inertial and gravity variations and actuated with transmissions as speed reducer, screw-nut or worm gear. A new joint friction model taking dynamic and external forces into account is proposed in this paper. A new identification process is proposed, merging all the joint data collected while the mechanism is tracking exciting trajectories and with different payloads, to get a global LS estimation in one step. An experimental validation is carried out with a prismatic joint composed of a Star high precision ball screw drive positioning unit

Dynamic Model Identification for 6-DOF Industrial Robots

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model