The present invention has for object a method for estimating the angular position of a rotor in relation to a stator in a rotary electric machine, such as an electric machine of an electric or hybrid motorisation system, comprising: estimating the angular position and/or of the rotation speed of the rotor by a method of injecting high frequency signals as long as the absolute value of the rotation speed of the rotor, derived from said angular position, is less than a first predefined threshold; estimating the angular position and/or of the rotation speed of the rotor by a model coming from a learning method as long as the absolute value of the rotation speed of the rotor, derived from said angular position, is greater than a second predefined threshold.

A machine learning apparatus includes: a state observation unit that observes a state variable composed of at least one of data relating to the number of errors that is an error between a position command relative to a rotor of a motor which is drive-controlled by the motor control apparatus and an actual position of a feed mechanism unit, an operation program of the motor control apparatus, any command of the position command, a speed command, or a current command in the motor control apparatus, data relating to a workpiece machining condition in a machine tool including the motor control apparatus, and data relating to a state of the machine tool including the motor control apparatus; and a learning unit that learns a condition associated with the number of corrections used to correct the above-mentioned command in accordance with a training data set constituted by the state variable.

Various embodiments of the teachings herein include a method for controlling a transport system with at least one linear and/or planar motor having a stator, at least one mover, and a controller for automatically controlling movement of the at least one mover relative to the stator. An example method includes: providing a thermal model for the transport system; determining a movement plan for the at least one mover according to a predefined movement task, running a simulation of a temperature distribution within the at least one linear and/or planar motor during the movement based on the thermal model such that the movement plan respects at least one predefined temperature limit within the simulation; and executing the determined movement plan on the transport system using the controller.

A motor controller is provided with degradation estimation circuitry configured to calculate an estimated degradation level obtained by estimating a degradation level of a device on which a motor is mounted or a degradation level of an inverter; operation decision circuitry configured to compare the estimated degradation level and a reference degradation level, which is predetermined, and set an operation mode of the motor, in a case in which the estimated degradation level is lower than the reference degradation level, to an normal operation, and set the operation mode, in a case in which the estimated degradation level is higher than or equal to the reference degradation level, to a low-noise pulse operation; and control circuitry configured to control the inverter according to the operation mode. The low-noise pulse operation is designed to reduce switching loss to be less than does the normal operation.

A parameter identification method for a synchronous motor based on a neural network is disclosed. A method for parameter identification of synchronous motors based on neural networks includes: S1) establishing and training a first neural network model to determine intermediate results of the parameters to be identified for the synchronous motor, S2) forming a second neural network model by adding a compensation module to the trained first neural network model, S3) training the second neural network model based on the intermediate results of the parameters to be identified, and determining the final results of the parameters to be identified using the trained second neural network model. Also disclosed is a device and a computer program product for parameter identification of synchronous motors based on neural networks.

A machine learning apparatus includes: a state observation unit that observes a state variable composed of at least one of data relating to the number of errors that is an error between a position command relative to a rotor of a motor which is drive-controlled by the motor control apparatus and an actual position of a feed mechanism unit, an operation program of the motor control apparatus, any command of the position command, a speed command, or a current command in the motor control apparatus, data relating to a workpiece machining condition in a machine tool including the motor control apparatus, and data relating to a state of the machine tool including the motor control apparatus; and a learning unit that learns a condition associated with the number of corrections used to correct the above-mentioned command in accordance with a training data set constituted by the state variable.

A machine learning apparatus includes: a state observation unit that observes a state variable including an error between a position command and an actual position of a rotor, temperature of a motor driving apparatus and the motor, and voltage of each part of the motor driving apparatus; and a learning unit that learns a current feedback offset correction value for correcting an offset in the current feedback value, an inter-current-feedback-phase unbalance correction value for correcting an unbalance between phases in the current feedback value, and a current command correction value for a dead zone for correcting a current command in order to compensate a decreased amount of current due to a dead zone by which switching elements of upper and lower arms in the same phase of an inverter for motor power supply are not simultaneously turned on, in accordance with a training data set defined by the state variable.

A method for operating an electrical drive system includes the steps of: acquiring a number of input variables pertaining to the electrical drive system; determining at least one state variable from the acquired input variables by way of an observer; determining at least one disturbance variable from the acquired input variables by way of the observer; controlling the electrical drive system on the basis of the at least one determined state variable; and monitoring the state of the electrical drive system by machine learning, the at least one disturbance variable forming input data for a machine learning model.

A processor for controlling a motor, a motor control device and a control method therefore are provided. The processor includes a feedback calculator, a control calculator and a drive calculator. The feedback calculator calculates a direct-axis current and a quadrature-axis current according to a drive current driving a motor and an operating angle of the motor. The control calculator includes a reinforcement learning controller. The reinforcement learning controller uses a reinforcement learning algorithm to calculate a direct-axis voltage and a quadrature-axis voltage according to a quadrature-axis current command, the direct-axis current and the quadrature-axis current. The quadrature-axis current command is obtained according to a reference rotational speed and the operating speed of the motor. The drive calculator generates a switching signal according to the direct-axis voltage, quadrature-axis voltage and an operating angle of the motor. The switching signal is used to control a driving circuit to drive the motor.

In a motor drive control device including a machine learning function, appropriate motor drive control in accordance with the usage environment of a motor is realized. A motor drive control device 10 includes: a measurement data generation unit 23 that generates measurement data 300 relating to operation of a motor 50; a training data generation unit 24 that attaches predetermined identification information indicating the operation state of the motor 50 to the measurement data 300 and generates training data 310; a machine learning unit 25 that generates a learned model 320 for determining the operation state of the motor 50 by performing machine learning using the training data 310; and a monitor control unit 26 that monitors the operation state of the motor 50 using the learned model 320. In the motor drive control device 10, the training data generation unit 24 starts generation of the training data 310 when the training data generation unit 24 receives a command ordering acquisition of the training data 310 from a host device 4.