Precise Torque Control in High Temperature with Heat Transfer Model based Torque Constant Compensation Algorithm

Abstract

Robot-driving motors are frequently driven at high temperatures as their weight-to-torque ratio increases for various movements of robots. Such an increase in driving temperature reduces the magnetic flux density of the permanent magnet and the torque constant of the motor. Particularly in applications that mainly utilize feed-forward torque control without an additional torque sensor, this torque constant reduction leads to severe degradation of torque control performance. This research proposes a torque control method that compensates for the torque constant depending on temperature by identifying the relationship between magnet temperature and the torque constant. In addition, since it is difficult to measure the temperature of the rotor-attached magnet directly, lumped parameter thermal network(LPTN) and full-state observer are used for magnet temperature estimation. The robustness of the proposed controller is verified through experimental results of torque error from 6.19% without compensation to 0.65% with compensation.