A novel motor controller controls a stepping motor and includes a command value calculator to calculate and output a speed command value and an angle command value based on a reference clock. A control method selector is included to select one of the open-loop control and the closed-loop control in accordance with the speed command value. A command value output unit is included to output a first target value as the first current command value when the open-loop control is selected and outputs a second target value as the second current command value when the closed-loop control is selected. The motor controller controls the stepping motor based on the first target value when the open-loop control is selected and the second target value when the closed-loop control is selected.

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 system for controlling a motor includes a controller module comprising a controller portion, a regulator portion and an integrator portion. The regulator portion includes a set of regulator modules communicatively coupled in a sequence. Each regulator module configured to receive a respective input signal, indicative of a target or selected value, from the controller portion or the immediately preceding regulator module in the sequence, determine a respective selectable value; select one of the respective selectable value and the value indicated by the received input signal; and provide the selected value as an output signal to the next regulator module in the sequence or the integrator module. The integrator module is configured to receive the output signal from the last regulator module in the sequence, calculate a final demand value based on the received signal, and provide an output signal indicative of the final demand value.

To provide a motor controller and an electric power steering apparatus which can suppress torque fluctuation resulting from low detection resolution of angle, with simple calculation, even in condition where the rotational speed of motor is low. A motor controller estimates an angle error correlation value correlated with the angle detection error based on the current command value when a change frequency of the angle detection value is lower than a cutoff frequency of feedback control system which controls a rotation state; corrects the current command value or the angle detection value based on the estimation value of angle error correlation value; and changes the estimation value of angle error correlation value so that the absolute value of angle detection error increases with respect to increase in the current command value, and the absolute value of angle detection error decreases with respect to decrease in the current command value.

An electronic device includes an artificial intelligence processor that is configured to generate second information for controlling an electric component including at least one of a drive unit that applies propulsion force to a human-powered vehicle, an electric adjustable seatpost, and an electric suspension in accordance with first information related to at least one of the human-powered vehicle, a rider of the human-powered vehicle, and an environment of the human-powered vehicle. The artificial intelligence processor changes a process for generating the second information in accordance with the first information and in accordance with an operation of a first operation unit for operating the at least one of the drive unit, the electric adjustable seatpost, and the electric suspension.

A human-powered vehicle control device includes an artificial intelligence processor, an operation device, and a communication device. The artificial intelligence processor is configured to generate second information for controlling an electric component of a human-powered vehicle in accordance with first information related to at least one of the human-powered vehicle, a rider of the human-powered vehicle, and an environment of the human-powered vehicle. The operation device operates the electric component. The communication device is configured to communicate with an external device. The artificial intelligence processor is configured to change a process for generating the second information in accordance with the first information and an operation of the operation device. The communication device is configured to transmit third information related to a process for generating the second information to the external device.

An apparatus according to the aspect of the embodiments includes a phase determiner configured to determine a rotational phase of a rotor, a speed determiner configured to determine a rotational speed of the rotor, a controller having a first control mode for controlling the motor by supplying constant currents to windings, and a discriminator configured to discriminate whether a rotation of the motor is abnormal based on the rotational speed when the rotational speed corresponding to a command speed is equal to or higher than a predetermined value in a state where the controller is controlling the motor in the first control mode. When a signal output from the discriminator indicates that the rotation of the motor is abnormal, the controller stops the motor. When the signal output from the discriminator indicates that the rotation is not abnormal, the controller continues a drive of the motor.

A transient current planning method for an ultra-high-speed permanent magnet synchronous motor for improving speed regulation response capabilities is provided. A transient current planning module uses a voltage model considering transient current changes to calculate current instruction values of an ultra-high-speed permanent magnet synchronous motor under MTPA control, general flux-weakening control, and MTPV control; a mode switching condition judgment subsystem judges whether a control mode is MTPA control or general flux-weakening control, or MTPV control, and sends d- and q-axis current instruction values in the corresponding control mode to a voltage decoupling control module; and the voltage decoupling control module calculates d- and q-axis voltage instruction values for controlling the motor, so as to realize control over the ultra-high-speed permanent magnet synchronous motor.

A battery state estimating apparatus as an embodiment includes a state estimator, a power estimator, and a determiner. The state estimator estimates a state of a battery. The power estimator estimates first power amount charged/discharged by the battery within a charging/discharging period, based on the state. The determiner compares the first power amount with second power amount inputted/outputted to/from the battery within the charging/discharging period and thereby determines validity of the state.

The first square wave control mode using the square wave pulse pattern is used when the voltage acting on the inverter is equal to or higher than the threshold voltage. On the other hand, when the voltage acting on the inverter is lower than the threshold voltage, a square wave pulse pattern is used when the rotation speed of the motor is equal to or higher than the first predetermined rotation speed that is higher than the first resonance region, and the second square wave control mode using the first switching pattern for suppressing the LC resonance in the first resonance region is used when the rotation speed of the motor is lower than the first predetermined rotation speed.