Identificación de los parámetros dinámicos de un robot mediante el filtro de kalman extendido

Se presenta en este trabajo la aplicación del filtro de Kalman Extendido al problema de identificación de los parámetros dinámicos de un brazo robot de revolución de 5 grados de libertad (GDL). Primeramente, las ecuaciones de estado del manipulador son ampliadas para incluir en el vector de estados los parámetros a identificar, por ejemplo, los momentos y productos de inercia en tres de las articulaciones. Con este modelo de estado, a partir de la medición de los torques (pares) aplicados a las articulaciones y las posiciones y velocidades articulares resultantes se pudieron estimar los parámetros dinámicos mediante el filtrado de las incertidumbres del sistema y de las observaciones

New Method for Global Identification of the Joint Drive Gains of Robots using a Known Inertial Payload

International audienceOff-line robot dynamic identification methods are mostly based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint force/torque that is linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In this paper it is proposed a new method for the identification of the total joint drive gains in one step, using available joint sampled data given by the standard controller of the moving robot and using CAD or measured values of the inertial parameters of a known payload. A new inverse dynamic model calculates the current reference signal of each joint j that is linear in relation to the dynamic parameters of the robot, to the inertial parameters of a known payload fixed to the end-effector, and to the inverse of the joint j drive gain. This model is calculated with current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with the known payload fixed on the robot. Each joint j drive gain is calculated independently by the weighted LS solution of an over-determined linear systems obtained with the equations of the joint j. The method is experimentally validated on an industrial Stäubli RX-90 robot

Trajectory Synthesis for Fisher Information Maximization

Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general, nonlinear dynamic systems, finding globally "best" trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix. A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the Fisher information matrix by three orders of magnitude compared to the initial trajectory. Experimental results show that this optimized trajectory translates to an order of magnitude improvement in the parameter estimate error in practice.Comment: 12 page

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

Global Identification of Robot Drive Gains Parameters Using a Known Payload and Weighted Total Least Square Techniques

International audienceOff-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In the previous works, it was proposed to identify each gain separately. This does not allow taking into account the dynamic coupling between the robot axes. In this paper the global joint drive gains parameters of all joints are calculated simultaneously. The method is based on the weighted total least squares solution of an over-determined linear system obtained with the inverse dynamic model calculated with available current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with a known payload fixed on the robot. The method is experimentally validated on an industrial 6 joint Stäubli TX-40 robot.

Global Identification of Drive Gains Parameters of Robots Using a Known Payload

International audienceOff-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In the previous works, it was proposed to identify each gain separately. This does not allow taking into account the dynamic coupling between the robot axes. In this paper the global joint drive gains parameters of all joints are calculated simultaneously. The method is based on the total least squares solution of an over-determined linear system obtained with the inverse dynamic model calculated with available current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with a known payload fixed on the robot. The method is experimentally validated on an industrial Stäubli TX-40 robot

Chapitre d'équation 1 Section 1

International audienceOff-line robot dynamic identification methods are mostly based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint force/torque that is linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In this paper it is proposed a new method for the identification of the total joint drive gains in one step using available joint sampled data given by the standard controller of the moving robot and using only the weighted mass of a payload, without any CAD values of its inertial parameters. A new inverse dynamic model calculates the current reference signal of each joint j that is linear in relation to the dynamic parameters of the robot, to the inertial parameters of a known mass fixed to the end-effector, and to the inverse of the joint j drive gain. This model is calculated with current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with the known mass fixed on the robot. Each joint j drive gain is calculated independently by the weighted LS solution of an over-determined linear systems obtained with the equations of the joint j. The method is experimentally validated on an industrial Stäubli RX-90 robot

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