The result of this operation is stored in an axis property called TorqueFeedback. Therefore, the softMC contains an axis property called TorqueFactor (TFac) which divides the motor's torque parameter in order to convert it to desirable units. The softMC reads the motor's torque through the drives, but not in units. This section describes the torque units for each system and how they relate to each other. Therefore, the torque can be defined with different units in each system. When dealing with motors, the torque is usually modeled as proportional to the current in the motors. The MKS system of units defines the torque unit as (Newton*meter). Then, the dynamic model parameters of the robot are extracted based on the robot model and the measurements.įigure 1 - A schematic drawing of the control system and the feedforward part of the Taddcmd Torque Units The identification process is performed as follows: while the robot is commanded to perform a certain movement, the movement parameters and internal motors torques are recorded. The softMC allows to perform measurements and estimate the dynamic model parameters of some common robot models. In such cases, those parameters can be estimated using the softMC identification process. In most cases, the user doesn’t have the parameters of the dynamic model. The joint velocity and torque are then sent to the drives as additional torque command data (see Figure 1). In order to be able to do that, it needs a dynamic model – i.e., a model of the masses and inertias – of the motion element. The term inverse dynamic model (often named computed torque control) means that the softMC takes a cartesian motion of a motion element and not only computes the joint position for the next Cartesian setpoint, but also the joint torque required for the motion. velocity and torque – to offset the command values of the torque controller in order to make them react faster and thereby reduce the final positioning error and improve the settling behavior. The motivation of the IDM feature is to use additional knowledge about the motion than just the position – i.e. The inverse dynamic model (IDM) feature is used to compensate friction and dynamic effects for motion elements, otherwise leading to positioning errors and suboptimal settling and tracking behavior. Introduction - what is it good for and how does it work 9.2 Identification process success but poor real time prediction results.9 How to assess what is the failure reason based on the output files.8.6 Wrong type of DynamicModel property.8.5 TorqueFactor properties are not configured well.8 Review of the possible reasons for an identification failure.6 Demonstration of tracking improvements (settling, position error).5 Explanation of the identification process recording file.4.3 Including the identification output file in setup.4 Explanation of the identification output file.3.4.2.2 Dynamic Model 2 - vertical or tilted axis.3.4.2.1 Dynamic Model 1 - horizontal axis.3.4.1.3 Dynamic Model 3 - vertical crank-arm axis.3.4.1.2 Dynamic Model 2 - horizontal crank-arm axis.3.4.1.1 Dynamic Model 1 – simple rotary axis.3.3 Identification process for different robot models.3 How to perform the dynamic model parameters identification.2.5.1 Rotary motors with lead screws / pulleys / or different type of mechanism used to translate turning motion into linear motion.2.2 EtherCAT and CANopen standart (DS402).1 Introduction - what is it good for and how does it work.
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