An adjustment device that controls a control device controlling motors driving at least two axes, and a machine learning device performing machine learning with respect to the control device. The adjustment device includes: a start command output unit configured to output a start command for starting the machine learning device; a feedback information. acquisition unit configured to acquire feedback information acquired on the basis of an evaluation program executed by the control device, from the control device; a feedback information transmission unit configured to transmit the feedback information acquired, to the machine learning device; a parameter setting information acquisition unit configured to acquire control parameter setting information acquired by machine learning using the feedback information, from the machine learning device; and a parameter setting information transmission unit configured to transmit the control parameter setting information acquired, to the control device.

A motor controller which controls a servo motor for driving a machine, includes: a speed command unit which commands the speed of the machine; a speed detection unit which detects the speed of the servo motor; and a speed control unit which produces a torque command based on a speed command and a motor speed detected so as to control the speed of the servo motor, where the speed control unit includes a filter which approximates the inverse characteristic of a transmission characteristic from the servo motor to the machine, the filter has a transmission characteristic F(s) based on a frequency , a vibration damping coefficient and a cutoff frequency .sub.adj which are adjustment parameters and the frequency is adjusted so as to be equal to or more than an antiresonant frequency .sub.0 of the machine but less than a resonant frequency .sub.p.

A motor controller which controls a servo motor for driving a machine, includes: a speed command unit which commands the speed of the machine; a speed detection unit which detects the speed of the servo motor; and a speed control unit which produces a torque command based on a speed command and a motor speed detected so as to control the speed of the servo motor, where the speed control unit includes a filter which approximates the inverse characteristic of a transmission characteristic from the servo motor to the machine, the filter has a transmission characteristic F(s) based on a frequency , a vibration damping coefficient and a cutoff frequency .sub.adj which are adjustment parameters and the frequency is adjusted so as to be equal to or more than an antiresonant frequency .sub.0 of the machine but less than a resonant frequency .sub.p.

A position control device includes a position detector that detects a position of an object to be controlled; a target generator that outputs a target velocity and a target position of the object to be controlled; a first calculator that calculates a control amount for causing the object to be controlled to track the target position; a second calculator that calculates a control amount for controlling the velocity of the object to be controlled, to be at the target velocity; a control signal output unit that outputs a control signal according to a total control amount obtained by the first and the second calculators; and a storage unit that stores an attitudinal change correction amount. The second calculator acquires the attitudinal change correction amount stored in the storage unit, adds the attitudinal change correction amount to the control amount to correct the control amount, and outputs the corrected control amount.

An adjustment device that controls a control device controlling motors driving at least two axes, and a machine learning device performing machine learning with respect to the control device. The adjustment device includes: a start command output unit configured to output a start command for starting the machine learning device; a feedback information. acquisition unit configured to acquire feedback information acquired on the basis of an evaluation program executed by the control device, from the control device; a feedback information transmission unit configured to transmit the feedback information acquired, to the machine learning device; a parameter setting information acquisition unit configured to acquire control parameter setting information acquired by machine learning using the feedback information, from the machine learning device; and a parameter setting information transmission unit configured to transmit the control parameter setting information acquired, to the control device.

An example method for controlling a carriage of a printing device is described. In one implementation, a target velocity value that satisfies a sharpness threshold is filtered by a processor resource of the printing device, a feed forward term is generated using a continuous model applied to the filtered velocity input using a plurality of electromechanical parameters, a feedback term is generated based on a difference between an expected pulse-width modulation (PWM) profile and an actual PWM profile, and a voltage to be provided to a motor of the carriage is adjusted based on the feed forward term and the feedback term.

An example method for controlling a carriage of a printing device is described. In one implementation, a target velocity value that satisfies a sharpness threshold is filtered by a processor resource of the printing device, a feed forward term is generated using a continuous model applied to the filtered velocity input using a plurality of electromechanical parameters, a feedback term is generated based on a difference between an expected pulse-width modulation (PWM) profile and an actual PWM profile, and a voltage to be provided to a motor of the carriage is adjusted based on the feed forward term and the feedback term.