Abstract: A search range of machine learning is changed and relearned when the search range is not appropriate.

Abstract: 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.

Abstract: A control system of a machine tool which machines a work includes: a numerical control device which controls the drive axis of the machine tool based on control data; a machined surface measurement device which measures the machined surface of the work; and a data processing device, and the data processing device includes a drive axis control data acquisition portion which acquires, from the numerical control device, the chronological control data when the work is machined; a machined surface measurement data acquisition portion which acquires spatial machined surface measurement data after the machining of the work measured by the machined surface measurement device; and a data-associating processing portion which associates the chronological control data acquired by the drive axis control data acquisition portion and the spatial machined surface measurement data acquired by the machined surface measurement data acquisition portion with each other.

Abstract: A machine learning device performs machine learning with respect to a servo motor controller that converts a three-phase current to a two-phase current of the d- and q-phase. The machine learning device includes: a state information acquisition unit configured to acquire, from the servo motor controller, state information including velocity or a velocity command, reactive current, and an effective current command and effective current or a voltage command; an action information output unit configured to output action information including a reactive current command to the servo motor controller; a reward output unit configured to output a value of a reward of reinforcement learning based on the voltage command or the effective current command and the effective current; and a value function updating unit configured to update a value function on the basis of the output value of the reward, the state information, and the action information.

Abstract: A motor drive system includes: a flywheel; a servomotor for buffer which includes a plurality of independent windings and allows the flywheel to rotate; a plurality of inverters for buffer respectively connected to the windings; a plurality of converters respectively connected to the DC links; inverters for drive which perform power conversion between a DC power in the DC links and an AC power which is a drive power or a regenerative power of the servomotor for drive; and a motor control unit for buffer configured to control driving of the servomotor for buffer by controlling power conversion of the respective inverters for buffer respectively connected to the windings.

Abstract: A plurality of evaluation functions and a machine learning result of each of the evaluation functions are output so that a relation between the evaluation function and the learning result can be ascertained. An output device includes: an output unit that outputs a plurality of evaluation functions used by a machine learning device that performs machine learning of parameters of components of a servo control device that controls a servo motor that drives an axis of a machine tool, a robot, or an industrial machine and a machine learning result of each of the evaluation functions; and an information acquisition unit that acquires the machine learning result from at least one of the servo control device and the machine learning device.

Abstract: An output device includes: an information acquisition unit which acquires, from a machine learning device that performs machine learning on a servo control device for controlling a servo motor driving the axis of a machine tool, a robot or an industrial machine, a parameter or a first physical quantity of a constituent element of the servo control device that is being learned or has been learned; and an output unit which outputs at least one of any one of the acquired first physical quantity and a second physical quantity determined from the acquired parameter, a time response characteristic of the constituent element of the servo control device and a frequency response characteristic of the constituent element of the servo control device, and the time response characteristic and the frequency response characteristic are determined with the parameter, the first physical quantity or the second physical quantity.

Abstract: An object is to make it possible to acquire a parameter or a first physical quantity that has been learned and an evaluation function value and thereby check the progress or the result of machine learning. An output device includes: an information acquisition unit which acquires, from a machine learning device that performs machine learning on a servo control device for controlling a servo motor driving the axis of a machine tool, a robot or an industrial machine, a parameter or a first physical quantity of a constituent element of the servo control device that is being machine learned or has been machine learned and an evaluation function value; and an output unit which outputs information indicating a relationship between the acquired parameter, the first physical quantity or a second physical quantity determined from the parameter and the evaluation function value.

Abstract: Setting of parameters that determine filter characteristics is facilitated. A machine learning device performs machine learning of optimizing coefficients of at least one filter provided in a servo control device that controls rotation of a motor. The filter is a filter for attenuating a specific frequency component. The coefficients of the filter are optimized on the basis of measurement information of a measurement device that measures at least one of an input/output gain and an input/output phase delay of the servo control device on the basis of an input signal of which the frequency changes and an output signal of the servo control device.

Abstract: A motor control device includes a pressure command unit calculating a pressure command for commanding pressure generated in a pressure control object, a pressure detection unit detecting the pressure generated in the pressure control object, a pressure control unit calculating a speed command for pressure control for the servo motor, based on the calculated pressure command and the detected pressure, and a servo control unit controlling speed of the servo motor, based on the calculated speed command. The pressure control unit performs an integral operation. When a direction of increasing pressure in the integral operation is defined as a positive direction in the integral operation, and a direction of decreasing the pressure is defined as a negative direction, a median between an upper limit in the positive direction and a lower limit in the negative direction in the integral operation is larger than zero.

Abstract: 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.

Abstract: To provide a characteristic judgment apparatus that can easily perceive a difference in characteristics of a target machine from other machines, a characteristic judgment method, and a characteristic judgment program. A characteristic judgment apparatus includes: a learning section that individually sets a parameter in accordance with an individual difference of a machine by machine learning; an acquisition section that acquires the parameter that is set; and a comparison section that compares a parameter of a target machine with a distribution of the parameter of a plurality of other machines, and outputs characteristic information that is unique to the target machine.

Abstract: A control system of a machine tool includes an analysis device, the analysis device includes acquisition portions which acquire chronological control data when a work is machined and which acquire spatial machined surface measurement data after the machining of the work, a storage portion which stores the control data and the machined surface measurement data, a data-associating processing portion which associates the control data and the machined surface measurement data with each other in two machining directions, a machined surface failure detection portion which detects a failure on the machined surface of the work and a location thereof based on the machined surface measurement data in the two machining directions and an identification portion which identifies a drive axis that causes the failure from the detected failure and the machining direction of the control data corresponding to the detected failure location.

Abstract: Setting of parameters that determine filter characteristics is facilitated. Machine learning of optimizing the coefficients of a filter provided in a motor control device that controls rotation of a motor for a machine tool, a robot, or an industrial machine is performed on the basis of measurement information of an external measuring instrument provided outside the motor control device and a control command input to the motor control device.

Abstract: A non-transitory computer readable information recording medium stores an evaluation program for operating first and second motor control units, for evaluating operation characteristics related to a control device including a first motor control unit configured to control a first motor driving a first axis, and a second motor control unit configured to control a second motor driving a second axis. The evaluation program operates the first and second motor control units so that a shape of a movement path of a control target moved by the first and second axes includes at least a cornered shape in which both rotation directions of the first and second motors do not invert, and an arc shape in which one of the first and second motors rotates in one direction, and a rotation direction of the other of the first and second motors inverts.

Abstract: 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.

Abstract: A servo motor control apparatus for controlling a servo motor includes a storage unit configured to store a torque constant predefined for the servo motor, a torque constant correction unit configured to correct the torque constant stored in the storage unit when magnetic saturation occurs in a winding of the servo motor, and an output calculation unit configured to calculate an output of the servo motor on the basis of the torque constant stored in the storage unit or the torque constant after correction calculated by the torque constant correction unit, a value associated with a current of the servo motor, and a value associated with a speed of the servo motor.

Abstract: A control system of a machine tool includes an analysis device, the analysis device includes acquisition portions which acquire chronological speed control data when a work is machined and which acquire spatial machined surface measurement data after the machining of the work, a data-associating processing portion which associates the speed control data and the machined surface measurement data with each other, a machined surface failure detection portion which detects a failure depth of a failure location on the machined surface of the work and an identification portion which identifies the control data of the failure location corresponding to the machined surface measurement data of the failure location so as to identify a failure depth corresponding to the control data of the failure location and the numerical control device corrects the control data based on the control data of the failure location and the corresponding failure depth.

Abstract: A motor drive system includes a power source unit configured to supply DC power to a DC link, a servo-amplifier for drive configured to convert the DC power in the DC link into AC power and supply the AC power as drive power to a servomotor for drive, a power storage device configured to store the DC power from the DC link or supply the DC power to the DC link, a power consumption calculation unit configured to calculate total power consumption as the sum of power consumed by the servomotor for drive, the servo-amplifier for drive and the power source unit, and a power storage device control unit configured to control power storage and power supply of the power storage device according to the total power consumption, wherein the power storage device control unit determines start and end of power storage or power supply, based on different thresholds.

Abstract: The settling time of machine learning is shortened. A machine learning device is configured to perform machine learning related to optimization of coefficients of a transfer function of an IIR filter of a feedforward calculation 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 using feedforward control by a feedforward calculation unit having the IIR filter. The machine learning device represents a zero-point at which the transfer function of the IIR filter is zero and a pole at which the transfer function diverges infinitely in polar coordinates using a radius r and an angle ?, respectively, and searches for and learns, within a predetermined search range, the radius r and the angle ? to thereby perform the optimization of the coefficients of the transfer function of the IIR filter.