Patent ID: 11914333
Assignee: FANUC CORPORATION
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 7:
8. A machine learning method for a machine learning device configured to perform machine learning with respect to a plurality of servo control units configured to control a plurality of motors, the motors configured to drive a machine having a plurality of axes, with one axis among the plurality of axes receiving interference generated by movement along at least one of the other axes,
a first servo control unit related to the one axis receiving the interference among the plurality of servo control units, the first servo control unit comprising compensation units including a position error compensation unit configured to obtain a compensation value for compensating for a position error, a velocity command compensation unit configured to obtain a compensation value for compensating for a velocity command, and a torque command compensation unit configured to obtain a compensation value for compensating for a torque command of the first servo control unit based on one or more functions including at least one of variables related to a position command and variables related to position feedback information of a second servo control unit related to the axis generating the interference, and
the machine learning method comprising the steps of:
acquiring state information including first servo control information of the first servo control unit, second servo control information of the second servo control unit, and coefficients for each of the position error compensation unit, the velocity command compensation unit, and the torque command compensation unit based on the one or more functions;
outputting action information including adjustment information for adjusting of the coefficients for each of the position error compensation unit, the velocity command compensation unit, and the torque command compensation unit of the compensation units included in the state information to the compensation units;
outputting a reward value each time an action is executed based on the adjusting of the coefficients for reinforcement learning using an evaluation function serving as a function of the first servo control information; and
updating a value function related to the adjustment information of the coefficients based on the reward value, the state information, and the action information.