Systems and methods of controlling a length of an air gap in an electric machine using an air gap controller may include: determining an air gap length value for an electric machine at least in part using an air gap controller, comparing the determined air gap length value to an air gap target value using the air gap controller, and outputting a control command from the air gap controller to a controllable device associated with an air gap control system when the determined air gap length value differs from the air gap target value by a predefined threshold. A control command may be configured to impart a change to an operating parameter associated with the air gap control system to adjust a length of an air gap between an outer surface of a rotor core and an inner surface of a stator core of the electric machine.

The invention relates to a rotary blood pump including a housing having an internal chamber, a blood inlet port and a blood outlet port, a rotor including a plurality of blades and being adapted to rotate within the chamber. The pump includes a bearing system for controlling the position of the rotor wherein the bearing system includes one or more permanent magnets embedded in the rotor and one or more electromagnetic field inducing means embedded in the housing. The magnets embedded in the rotor are influenced by the electromagnetic field inducing means embedded in the housing for controlling the position of the rotor relative to the internal chamber of the housing and for driving rotation of the impeller within the chamber of the housing.

Example implementations of the present disclosure are directed to systems and methods directed to increasing the accuracy and speed that anomalous and malicious network data can be identified within a vehicle. Through the utilization of example implementations described herein, the security of the vehicle can be increased and the risk of a vehicle's internal systems being compromised and property being damaged can be reduced.

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 motorization 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 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 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 system in a vehicle includes a controller to implement a neural network to provide current commands based on inputs. The inputs include a torque input. The system also includes a current controller to provide a three-phase voltage through an inverter based on the current commands from the controller. An electric traction motor provides drive power to a transmission of the vehicle based on injection of the three-phase voltage. The current commands resulting from implementation of the neural network are corrected based on estimated torque resulting from the injection of the three-phase voltage to the electric traction motor.

A method for controlling a planar drive system includes generating a position allocating function, in an allocation generating step; measuring a plurality of measuring values of the magnetic rotor field by magnetic field sensors for a position of the rotor relative to the stator module, in a magnetic rotor field determining step; applying the position determination function to the plurality of measuring values of the magnetic rotor field of the plurality of magnetic field sensors, in a measuring value analysis step; and determining the position of the rotor relative to the stator module on the basis of the measurements of the magnetic rotor field measured by the plurality of magnetic field sensors and based on the allocations of the position allocating function, in a position determining step. The application further relates to such a planar drive system.

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.

Example implementations of the present disclosure are directed to systems and methods directed to increasing the accuracy and speed that anomalous and malicious network data can be identified within a vehicle. Through the utilization of example implementations described herein, the security of the vehicle can be increased and the risk of a vehicle's internal systems being compromised and property being damaged can be reduced.