Embodiments of the present disclosure provide for a quantum computing system with one or more integrated servo systems. In some embodiments, the one or more integrated servo systems may be dynamically tuned or adjusted, which may provide for reduction of noise and improvement of timing of execution of quantum operations in the quantum computing system. In some embodiments, the integrated servo systems may be configured to use closed loop control system to tune the integrated servo systems. In some embodiments, the integrated servo systems may be configured for live streaming to operators through a plurality of operations.

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.

A controller capable of preventing a control target from being controlled by an abnormal value output from a mathematical model is provided. A controller includes a control unit, the control unit including: a first correction amount computation unit that computes a first correction amount for correction from a command value to a second command value; a second correction amount computation unit that computes a second correction amount for correction from the command value to the second command value; and a correction amount selecting unit that selects either one of the first correction amount and the second correction amount. The first correction amount computation unit computes the first correction amount using a first mathematical model configured by machine learning, and the second correction amount computation unit computes the second correction amount using a second mathematical model configured by a method different from that of the first correction amount computation unit.

Disclosed are a buffer material conveying and winding device, a buffer material conveying method and an assembly binding system, belonging to the technical field of a chip on glass. The buffer material conveying and winding device comprises a rack, a feed roller, a feed servo motor, a recycling roller, a recycling torque motor, a measuring device, a controller and an alarm. The controller may monitor whether the recycling length meets the requirement through the measuring device. The buffer material conveying method is based on the buffer material conveying and winding device. The assembly binding system comprises a buffer material conveying and winding device, a buffer material and a pressing head device.

To make it possible to evaluate quantitatively whether there is a problem on a machining surface. A simulator includes a memory unit that stores machining position data to be used when a machine tool machines a machining-target object, a machining surface simulation unit that uses the machining position data that is stored to perform a simulation of a machining surface, a surface texture calculation unit that calculates a surface texture of the machining surface that is simulated through the simulation of the machining surface, and a machining surface evaluation unit that evaluates the surface texture on the basis of an evaluation condition.

A servomotor control device includes a connection mechanism configured to connect a servomotor and a driven body, and transmit power of the servomotor to the driven body, a position command generation unit configured to generate a position command value for the driven body, a motor control unit configured to control the servomotor using the position command value, a force estimation part configured to estimate drive force acting on the driven body at a connecting part with the connection mechanism, a switching part configured to switch between a first force estimated value estimated by the force estimation part and a second force estimated value that is a fixed value, and a compensation amount generation part configured to generate a compensation amount for compensating the position command value generated by the position command generation part, based on the first or second force estimated value that was switched to by the switching part.

A machine learning device performs machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device that includes a compensation generation unit configured to generate a compensation value to be added to a control command for controlling a servo motor and a limiting unit configured to limit the compensation value or the control command to which the compensation value is added so as to fall within a setting range. During a machine learning operation, when the compensation value or the control command is outside the setting range and the limiting unit limits the compensation value or the control command so as to fall within the setting range, the machine learning device applies the compensation value to the learning and continues with a new search to optimize the compensation value generated by the compensation generation unit.

An adaptive control device according to the present invention includes: a processor that generates a second command value for a motor on the basis of at least one of a first command value received from a numerical control device, feedback data received from a motor control device that controls the motor on the basis of the first command value, and sensor data received from a sensor; and a communication circuit that transmits the second command value to the numerical control device.

A servo controller includes: a servo control unit that controls a motor in a machine based on a position command; a position detection unit that detects the position of the motor; a positional error calculation unit that calculates a positional error between the position command and the position of the motor; a position abnormality detection unit that detects a position abnormality in the motor when the positional error is a first threshold or more; a load detection unit that detects a load torque on the motor; a load abnormality detection unit that detects a load abnormality in the motor when the load torque is a second threshold or more; an abnormality detection unit that detects an abnormality in the machine when the position abnormality or the load abnormality is detected; and a threshold change unit that changes the first or second threshold in response to the load state of the motor.

To provide a controller capable of exerting acceleration/deceleration control more accurately than has been exerted conventionally and capable of reducing the occurrence of shock and shortening cycle time. A numerical controller outputs a movement command for a drive axis of a machine based on a command in a program for controlling the machine having the drive axis controlled by a servo motor. The numerical controller exerts acceleration/deceleration control over the drive axis so as to satisfy a condition for the acceleration/deceleration in each of a machine coordinate system as an orthogonal coordinate system in the machine and a drive axis coordinate system by normalizing each of acceleration/deceleration related information in the machine coordinate system and acceleration/deceleration related information in the drive axis coordinate system to a value in the drive axis coordinate system.