Abstract: A machine learning device performs machine learning with respect to a plurality of servo control units corresponding to a plurality of axes. A first servo control unit related to an axis receiving interference includes a compensation unit that compensates for at least one of a position error, a velocity command, and a torque command of the first servo control unit based on at least one of a variable related to a position command and a variable related to position feedback information of a second servo control unit related to an axis generating the interference. The machine learning device acquires state information including first and second servo control information and a coefficient of the function, outputs action information and a reward value for reinforcement learning, and updates a value function on the basis of the reward value, the state information, and the action information.

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: The present invention performs simulation for a case where a plurality of degrees are set for feedforward, and provides assistance in adjustment of an acceleration/deceleration time constant and a feedforward parameter. The present invention is provided with: a mechanical model creation unit that creates mechanical models of a motor and a mechanism portion of a machine tool, a robot, or an industrial machine; a simulation unit that includes the mechanical models and a feedforward processing section and that is for performing simulation of operation of a servo control device for controlling the motor; and an adjustment unit that adjusts an acceleration/deceleration time constant for generating a position command, and a parameter of the feedforward processing section. The adjustment unit adjusts a plurality the acceleration/deceleration time constants and parameters corresponding to when a plurality of degrees has been set for the feedforward processing section.

Abstract: The present invention is to determine allocation of filters in such a manner as to satisfy a restriction for the number of filters while realizing suppression of resonance. The present invention provides a control assistance device assisting in setting a plurality of first filters provided in a servo control device controlling a motor, the control assistance device including: a resonance detection unit for detecting a plurality of resonance points in frequency characteristics including input-output gain and input-output phase lag of the servo control device, the frequency characteristics being measured on the basis of an input signal and an output signal having a varying frequency; a filter setting unit for setting a plurality of second filters the number of which is larger than the number of said plurality of filters in order to suppress the plurality of resonance points; and a grouping unit for grouping the plurality of second filters to set the plurality of first filters.

Abstract: A control assistance device implements assistance in order to adjust a coefficient for a plurality of filters provided to a servo control device. The control assistance device comprises: a resonance detection unit that detects a plurality of resonance points in frequency characteristics between an input/output gain and an input/output phase shift, of the servo control device, measured on the basis of an input signal and an output signal having varying frequencies; and a resonance evaluation unit that calculates the priority levels of the plurality of resonance points. The resonance evaluation unit calculates the priority level on the basis of a distance between a point (-1,0) or a point (k, 0) (where k is a value less than -1) on a real axis on a complex plane, and a resonance point on a Nyquist locus calculated from the frequency characteristics between the input/output gain and the input/output phase shift.

Abstract: A control assist device according to the present invention uses first information and second information, which comprise at least one filter coefficient of a servo control device that controls a motor and/or feedback gain, prior to or after being adjusted, calculates at least one frequency characteristic from among input/output gain between filter and feedback gain, and phase lag, prior to or after the filter coefficient and/or feedback gain is adjusted, and obtains an estimated frequency characteristic value for the servo control device input/output gain and phase lag after the filter coefficient and/or feedback gain is adjusted, on the basis of at least one frequency characteristic prior to or after being adjusted and the measured frequency characteristics of servo control device input/output gain and phase lag prior to the coefficient and/or feedback gain being adjusted.

Abstract: A machine learning device which performs machine learning to optimize at least one of a coefficient of a filter and a feedback gain, the machine learning device comprising: a state information acquiring unit which acquires state information including the at least one of the coefficient and the feedback gain and including input/output gain and input/output phase delay of a servo control device; an action information output unit which outputs action information including adjustment information for the at least one of the coefficient and the feedback gain; a reward output unit which obtains and outputs a reward on the basis of whether a Nyquist plot calculated from the input/output gain and the input/output phase delay passes through the inside of a closed curve; and a value function updating unit which updates a value function on the basis of the value of the reward, the state information, and the action information.

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: A motor controller includes: a command unit which outputs a command value for controlling a motor that drives a driven unit; a motor control unit which controls the motor based on the command value; a compensation filter which compensates for the command value; and a preprocessing unit which is provided in a stage preceding the compensation filter, the compensation filter has a frequency region in which a gain is greater than 1 and the preprocessing unit executes, when a variation in the command value before being compensated for with the compensation filter is equal to or less than a predetermined value, preprocessing in which a past command value is used as a current command 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 machine learning device that searches for a first parameter of a component of a servo control device that controls a servo motor includes: a solution detection unit that acquires a set of evaluation function values used during machine learning or after machine learning, plots the set of evaluation function values in a search range of the first parameter or a second parameter used for searching for the first parameter, and detects whether a search solution is at an edge of the search range or is in a predetermined range from the edge; and a range changing unit that changes the search range to a new search range of the first parameter or the second parameter based on the estimation made on evaluation function values of an evaluation function expression when the search solution is at the edge of the search range or is in the predetermined range.

Abstract: A control device includes: a machine learning unit that machine-learns a control parameter that determines operating characteristics of a driving unit of a machine serving as a driving target of a motor and sets the control parameter to a motor control device; a health check operation instruction unit that outputs an instruction for a health check operation of driving the motor control device; an operation evaluation unit that acquires information indicating the operating characteristics of the driving unit, calculates an evaluation value on the basis of an evaluation function, and stores the evaluation value in a storage unit; and a deterioration estimation operation unit that estimates deterioration in the operating characteristics of the driving unit of the machine on the basis of the evaluation value stored in the storage unit and the evaluation value calculated by the operation evaluation unit when the health check operation was performed.

Abstract: A machine learning device that performs reinforcement learning for a servo control device and optimizes a coefficient of a filter for attenuating a specific frequency component provided in the servo control device includes a state information acquisition unit which acquires state information that includes the result of calculation of at least one of an input/output gain of the servo control device and a phase delay of input and output, the coefficient of the filter and conditions, and an action information output unit which outputs, to the filter, action information including adjustment information of the coefficient. A reward output unit determines evaluation values under the conditions based on the result of the calculation to output, as a reward, the value of a sum of the evaluation values. A value function updating unit updates an action value function based on the value of the reward, the state information and the action information.

Abstract: Vibration of a machine end and an error of a moving trajectory are suppressed. A machine learning device performs machine learning of optimizing first coefficients of a filter provided in a motor controller that controls a motor and second coefficients of a velocity feedforward unit of a servo control unit provided in the motor controller on the basis of an evaluation function which is a function of measurement information after acceleration and deceleration by an external measuring instrument provided outside the motor controller, a position command input to the motor controller, and a position error which is a difference between the position command value and feedback position detection value from a detector of the servo control unit.

Abstract: To provide an adjusting device and an adjusting method for appropriately controlling the machine learning reduced in cost with respect to calculation load and learning period of time in the case where an evaluation program for machine learning is used separately from a machining program and the like.

Abstract: An output device with which an operator can understand a progress state of machine learning from evaluation function values is provided. The output device includes: an information acquisition unit that acquires a plurality of evaluation function values which use servo data or are calculated using the servo data from a machine learning device that performs machine learning with respect to a servo control device that controls a servo motor that drives a shaft of a machine tool, a robot, or an industrial machine; and an output unit that outputs the plurality of acquired evaluation function values. The output unit may include a display unit that displays the plurality of evaluation function values on a display screen.

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: 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.

Abstract: A machine learning device performs machine learning with respect to a servo control device including at least two feedforward calculation units among a position feedforward calculation unit configured to calculate a position feedforward term on the basis of a position command, a velocity feedforward calculation unit configured to calculate a velocity feedforward term on the basis of a position command, and a current feedforward calculation unit configured to calculate a current feedforward term on the basis of a position command. Machine learning related to the coefficients of a transfer function of one feedforward calculation unit among the at least two feedforward calculation units is performed earlier than machine learning related to the coefficients of a transfer function of the other feedforward calculation unit.

Abstract: A machine learning device performs machine learning with respect to a plurality of servo control units corresponding to a plurality of axes. A first servo control unit related to an axis receiving interference includes a compensation unit that compensates for at least one of a position error, a velocity command, and a torque command of the first servo control unit based on at least one of a variable related to a position command and a variable related to position feedback information of a second servo control unit related to an axis generating the interference. The machine learning device acquires state information including first and second servo control information and a coefficient of the function, outputs action information and a reward value for reinforcement learning, and updates a value function on the basis of the reward value, the state information, and the action information.