A servo control system for controlling a plurality of axes of a machine tool, comprises: a plurality of servo control units for controlling the plurality of axes, respectively; a plurality of learning control units that are provided one each in the plurality of servo control units, and each configured to control a cyclic operation highly precisely; a common learning memory for storing correction data which at least a portion of the plurality of learning control units generates; a memory allocation unit for allocating at least a portion of a memory area in the learning memory to the axis that the learning control unit that generated the correction data controls; and a memory amount notifying unit for notifying the memory allocation unit as to the amount of memory that each of the plurality of learning control units of the respective axes requires.

Training and/or using a recurrent neural network model for visual servoing of an end effector of a robot. In visual servoing, the model can be utilized to generate, at each of a plurality of time steps, an action prediction that represents a prediction of how the end effector should be moved to cause the end effector to move toward a target object. The model can be viewpoint invariant in that it can be utilized across a variety of robots having vision components at a variety of viewpoints and/or can be utilized for a single robot even when a viewpoint, of a vision component of the robot, is drastically altered. Moreover, the model can be trained based on a large quantity of simulated data that is based on simulator(s) performing simulated episode(s) in view of the model. One or more portions of the model can be further trained based on a relatively smaller quantity of real training data.

A motor control apparatus including a controller that controls a servo motor or a spindle motor and includes a switching determining part that determines a switching condition of the controller based on axis position information on a motor related to control of the motor control apparatus, a machine learning part that adjusts one or more parameters for the controller by machine learning for each switching condition, and a parameter holding part that holds the parameter adjusted by the machine learning part for each switching condition. The switching determining part, when determining the switching condition after adjustment of the parameter, uses the adjusted parameter corresponding to the switching condition in the controller. The apparatus enables changing, and automatic adjustment, of a parameter or controller to be used depending on a switching condition of the parameter related to axis position information or a switching condition of the controller using the parameter.

A method, according to the present invention, of adjusting control parameters used in a control apparatus of an electric motor includes the steps of: computing a first frequency characteristic (Step 1); computing a present speed-proportional gain range (Step 2); computing a present mechanical-system characteristic constant (Step 3); computing a present proportional gain range (Step 4); computing a secular characteristic (Step 5); computing a secular speed-proportional gain range (Step 6); computing a secular proportional gain range (Step 7); and selecting proportional gain values (Step 8).

Provided are a controller and a machine learning device that perform machine learning to optimize the servo gain of a machine inside a facility in accordance with action conditions, action environments, and a priority factor of the machine. The control system observes machine information on a machine as state, acquires information on machining by a machine as determination data, calculates a reward based on the determination data and reward conditions, performs the machine learning of the adjustment of the servo gain of the machine, determines an action of adjustment of the servo gain of the machine based on the state data and a machine learning result of the adjustment of the servo gain of the machine, and changes the servo gain of the machine, based on the action of adjustment of the determined servo gain.

A machine learning device performs machine learning with respect to a servo control device including a velocity feedforward calculation unit. The machine learning device comprises: a state information acquisition unit configured to acquire from the servo control device, state information including at least position error, and combination of coefficients of a transfer function of the velocity feedforward calculation unit; an action information output unit configured to output action information including adjustment information of the combination of coefficients included in the state information, to the servo control device; a reward output unit configured to output a reward value in reinforcement learning based on the position error included in the state information; and a value function updating unit configured to update an action value function on the basis of the reward value output by the reward output unit, the state information, and the action information.

A machine learning device includes: a training data acquisition unit configured to acquire multiple pieces of training data each including insulation resistances of a servomotor at the beginning and the end of a certain period and time-series data indicating conditions of the servomotor in the certain period; and a learning model generating unit configured to perform a supervised learning using the training data to thereby generate a learning model.

Provided is a machine learning device configured to perform machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine, and that includes at least one feedback loop, a compensation generation unit configured to generate a compensation value to be applied to the feedback loop, and an abnormality detection unit configured to detect an abnormal operation of the servo motor, wherein, during a machine learning operation, when the abnormality detection unit detects an abnormality, the compensation from the compensation generation unit is stopped and the machine learning device continues optimization of the compensation value generated by the compensation generation unit.

Learning related to a device having a driving unit is performed more easily. An information processing device includes: a storage unit that stores a machining program for operating a motor of a machine tool, a robot, or an industrial machine; and a generation unit that generates a learning program for performing learning based on operating characteristics of the motor by extracting a partial machining program including a characteristic element from the machining program stored in the storage unit.

A controller that performs, for one or more axes of a machine, position control by taking friction into consideration includes a data acquisition unit acquiring at least a position command and a position feedback and a compensation torque estimation unit estimating coefficients of a friction model used when the position control is performed, on the basis of a position deviation which is a difference between the position command and the position feedback.