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.

An artificial intelligence device is disclosed. In an embodiment, the artificial intelligence device includes a sensor configured to acquire an output value according to control of a control system, and an artificial intelligence unit comprising one or more processors configured to obtain one or more updated parameters of a control function of the control system based on the output value using reinforcement learning, and update the control function for providing a control value to the control system with the one or more updated parameters.

An objective of the present invention is to provide a control device, control system, control method, and computer-readable storage medium, for enabling verification of the reliability of operation machine control. Provided is a control device comprising: a control part comprising a controller for outputting output data with regard to input data, said control part serving to control an operation machine using the controller; an acquisition part for acquiring attribute information including statistics of previously obtained input data and output data; and an evaluation part for, on the basis of a comparison of the attribute information with new input data being newly inputted into the controller and/or new output data being newly outputted from the controller with regard to the new input data, evaluating to what extent the new input data and/or the new output data deviate from the statistics.

A method for devising an optimum control policy of a controller for controlling a system includes optimizing at least one parameter that characterizes the control policy. A Gaussian process model is used to model expected dynamics of the system. The optimization optimizes a cost function which depends on the control policy and the Gaussian process model with respect to the at least one parameter. The optimization is carried out by evaluating at least one gradient of the cost function with respect to the at least one parameter. For an evaluation of the cost function a temporal evolution of a state of the system is computed using the control policy and the Gaussian process model. The cost function depends on an evaluation of an expectation value of a cost function under a probability density of an augmented state at time steps.

A machine learning includes a state observation unit that observes, as state variables representing a current state of an environment, PID control parameter data indicating the a parameter of the PID control during machining, machining condition data indicating a machining condition of the machining, and machining environment data relating to a machining environment of the machining, a determination data acquisition unit that acquires, as determination data, tool life determination data indicating an appropriateness determination result relating to depletion of the life of a tool during the machining, and cycle time determination data indicating an appropriateness determination result relating to the cycle time of the machining, and a learning unit that learns the machining condition and the machining environment of the machining, and the parameter of the PID control in association with each other.

A method of temperature control is applied to a cabinet in which a fan is disposed and the fan runs based on an input signal. The method includes obtaining a temperature signal corresponding to the cabinet, calculating a deviation signal between the input signal and the temperature signal, executing fuzzy learning to obtain a first proportional parameter, a first integral parameter and a first derivative parameter based on the deviation signal, generating a driver signal to execute a proportional-integral-derivative (PID) control based on the first proportional parameter, the first integral parameter and the first derivative parameter, wherein the driver signal is for driving the fan to adjust a temperature of the cabinet.

A control device of a wire electric discharge machine and a machine learning device are provided that can appropriately and readily determine a correction parameter. The control device, which optimizes the correction parameter for wire electrical discharge machining process, includes a machine learning device configured to learn the correction parameter for the wire electrical discharge machining process. The machine learning device includes a state observation unit configured to observe, as a state variable, condition data indicative of a condition for the wire electrical discharge machining process, a determination data acquisition unit configured to acquire determination data indicative of the correction parameter of the case where machining precision is favorable in the wire electrical discharge machining process, and a learning unit configured to learn the correction parameter in association with the condition for the wire electrical discharge machining process using the state variable and the determination data.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an optical parameter related to an optical system imaging a target object, by using machine learning, detect the target object on the basis of an imaging result in the optical system by using the calculated optical parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an operation parameter related to an operation of a robot by using machine learning, and control the robot on the basis of the calculated operation parameter.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an image processing parameter related to image processing on an image of a target object captured by a camera, by using machine learning, detect the target object on the basis of an image on which the image processing is performed by using the calculated image processing parameter, and control a robot on the basis of a detection result of the target object.