The quality of industrial robots essentially depends on the properties of their kinematic couples. This research has involved conducting an experimental study of the friction torque in a joint of the KUKA KR10 industrial robot and building its model. It has been established that the largest impact on friction in the joint is caused by its axial load and velocity, as well as the temperature of the mechanism, which is generally not homogeneous. It is not possible to measure temperature fields in the joints of a serial industrial robot directly. This study has set forth a method to estimate friction torque taking into account the temperature factor indirectly. For this, we have used the motor temperature available for measuring in combination with special periodical motions, performed by the robot, during which we estimated the actual friction torque in the joint and calculated a temperature correction based on our findings.
AbsIracI-This paper analyzes the metbod of synthesis of the controller is investigated assuming the plant dynamics uncertainty. The controller is designed using Model Predictive Control methodology, including discrete time-regressioo predictive model of the forward dynamics of the plant based on linear neural network structure. The model is parameterized via offline learning an the experimental data, obtained from the plant. The method of determining the tolerant prediction horizon value is proposed for tuning the controller, The control system structure for single-link flexible manipulator is considered using two predictive controllers ria hub angular velocity control loop and flexible link angular tip position control loop. The coutrol system is tested with both the computer simulation with MATLAB and controlling the laboratory model of the planar single-link flexible manipulator. The test has proved the effective vibration suppression and tip force disturbance rejection of the flexible link
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