A control system for controlling a power generator of a power generation system executes a control policy to map an input-and-output sequence to a current value of the excitation voltage, submits the current value of the excitation voltage to the power generator, accepts a current value of the rotor angle caused by actuating the power generator according to the current value of the excitation voltage, and updates the input-and-output sequence with the corresponding current values of the rotor angle and the excitation voltage. The input-and-output sequence of values of the operation of the power generator includes a sequence of multiple values of the rotor angle of the power generator and a corresponding sequence of multiple values of excitation voltage to the power generator causing the values of the rotor angle. The control policy maps the input-and-output sequence to a current control input defining the current value of the excitation voltage.

Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

A machine learning apparatus for learning a correction parameter used in correction of a command value that controls a motor in a motor drive system including a plurality of kinds of correction functions includes: a state observation unit that observes, as a state variable, each of a feature calculated on the basis of drive data and the kind of any of the correction functions of the motor drive system and the correction parameter; and a learning unit that learns the correction parameter for each of the correction functions according to a training data set created on the basis of the state variable.

A rotary machine control apparatus includes: a current detector detecting an alternating current flowing through a rotary machine and outputting a current detection value; a power converter supplying power to the rotary machine by applying an AC voltage based on a voltage command value; a current controller adjusting the voltage command value so that the current detection value matches a current command value; an estimator obtaining a magnetic-flux estimation value that is an estimation value of an amplitude of a magnetic flux vector in the rotary machine; and a magnetic flux controller adjusting the current command value so that the magnetic-flux estimation value matches a set magnetic-flux command value in a start-up control period from when the rotary machine is put in a state where the rotary machine rotates by inertia after interruption of power supply of the power converter until the power supply is resumed.

A steer by wire system for a vehicle includes a hand wheel, a steering gear that is attached to at least one steered road wheel, and at least one actuator that is connected to the hand wheel or the steering gear for the vehicle to apply to torque to the hand wheel or steering gear. The steer by wire system can include a control circuit comprising a first PID Controller which receives at an input a set point signal and provides as an output a control signal that is used to control the motor, the controller being arranged in a closed loop with the motor and configured to minimise an error value indicative of the difference between the demanded behaviour of the motor as indicated by the set point signal and the actual behaviour of the motor.

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.

According to various embodiments, a motor control circuit is described having a controller configured to determine values of a plurality of control voltages for a motor. The motor control circuit includes one or more current sensors configured to measure a plurality of operation currents of the motor and a neural network having a multi-layer perceptron architecture. The neural network is trained to estimate a rotor position of the motor for a current control cycle. The controller is configured to determine values of the plurality of control voltages for the current control cycle using the estimate of the rotor position.

The present disclosure provides a permanent-magnet fault-tolerant in-wheel motor based on an active sensorless strategy and drive and design methods thereof. The present disclosure proposes the permanent-magnet fault-tolerant in-wheel motor drive system based on an active sensorless strategy by considering sensorless operation performance in advance in a motor design stage. The present disclosure adopts fractional-slot concentrated windings, and ingeniously arranges alternating poles, a multi-layer magnetic barrier, and auxiliary permanent magnets, thus improving a sensorless operation accuracy of the motor while ensuring fault tolerance of the motor. The present disclosure proposes a frequency-band-adaptive secondary harmonic suppression strategy at a control layer to suppress an influence of a secondary salient harmonic on position observation and improve dynamic response performance of a system.

A motor control system includes a motor including a plurality of windings, a first sensor configured to sense a first operating parameter of the motor, a second sensor configured to sense a second operating parameter of the motor, and memory hardware configured to store a machine learning model and computer-executable instructions. The machine learning model is trained to generate a winding temperature estimation output based on motor operating parameter inputs. The motor control system includes processor hardware configured to execute the instructions and use the machine learning model to cause the motor control system to generate a winding temperature estimation output using the machine learning model based on the first operating parameter and the second operating parameter, the temperature estimation output indicative of a predicted temperature of the plurality of windings, and control the motor based on the winding temperature estimation output.