One or more example embodiments provide systems and methods for using impedance separation and impedance shaping.

A multistep finite control set model predictive control method for linear induction machines is provided, and the method belongs to a control technology field for linear induction machines. The method specifically includes the following steps: collecting a primary phase current of a linear induction machine in a three-phase coordinate system; solving a multistep reference voltage vector sequence by iteration according to a current value; keeping only two non-zero voltage vectors and one zero voltage vector that are closest to a reference voltage vector in each predictive step; and further eliminating a voltage vector sequence having a large cost function value through dynamic online comparison with a cost function value.

A system and method controls a rotor angular speed of an induction motor by first sensing an operation condition of the induction motor to produce measured signals, which are transformed by applying a state transformation to an induction motor model to produce a transformed induction motor model. Transformed state estimates of the transformed induction motor model are produced based on the measured signals. An inverse of the state transformation is applied to the transformed state estimates to produce state estimates of the induction motor model, which are then used to determine control input voltages for the induction motor, based on the state estimates, to control the rotor angular speed of the induction motor.

A method of calibrating a motor assembly includes selecting an electric motor and a motor controller for the motor assembly, obtaining at least one electric motor parameter of the electric motor, determining a correction factor for the electric motor based upon the at least one electric motor parameter, and programming the motor controller with the correction factor.

A method of calibrating a motor assembly includes selecting an electric motor and a motor controller for the motor assembly, obtaining at least one electric motor parameter of the electric motor, calculating a correction factor for the electric motor based upon the at least one electric motor parameter, and programming the motor controller with the correction factor.

A speed estimating device for an AC motor includes: a model deviation computing unit computing a model deviation based on a voltage, a current, and an estimated angular velocity of the AC motor; a first angular velocity estimating unit computing a first estimated angular velocity based on the model deviation; a second angular velocity estimating unit computing a second estimated angular velocity differing from the first estimated angular velocity in frequency, based on the model deviation; a compensation phase computing unit computing a compensation phase based on a disturbance frequency; and an estimated angular velocity calculator computing an estimated angular velocity of the AC motor based on the first estimated angular velocity and the second estimated angular velocity. Either one of the first estimated angular velocity and the second estimated angular velocity is computed based on the compensation phase.

In a method for determining the rotor position of a three-phase machine without using a rotary encoder, and to a device for controlling a three-phase motor without using a rotary encoder, the three-phase machine is fed by a converter that can be operated by pulse-width modulation, and the converter has model variables for the rotor angle and the current indicator of the three-phase machine, and the converter has device(s) by using which, in control operation, at least two values are measured which represent a measure of the local inductances of the machine which represent a measure of the local inductances of the machine, the error of the model rotor angle is determined in that, depending on the model rotor angle and the model current indicator, at least two weighting factors are determined, and in that a weighted sum is formed from the at least two measured values and the at least two weighting factors, and in that a further offset value is substracted from the sum, which is likewise determined on the basis of the model rotor angle and the model current indicator.

The disclosure discloses a linear oscillatory machine control method based on an adaptive full-order displacement observer. The method includes the following steps: a full-order displacement observer is constructed; the system equivalent spring elasticity coefficient and equivalent damping coefficient are used as parameters to be identified, the full-order displacement observer serves as an adjustable model, and the machine itself serves as a reference model; an error state equation is constructed based on the error of the adjustable model and the reference model; Popov's hyperstability theory is adopted to obtain the operation upper critical frequency and the adaptive formula of adjustable parameters that make the error state equation converge; the machine is operated at a frequency lower than the upper critical frequency, the machine current and voltage signals measured during the operation process are input into the full-order displacement observer, thereby realizing frequency tracking and displacement control.

A method for determining a motor angle, may include deriving a sensor weight and a sensorless weight via a cross product of an actual current vector and a model-based sensored current vector and a cross product of the actual current vector and a model-based sensorless current vector; and determining a final motor angle by applying the sensor weight and the sensorless weight to each of a sensored angle and a sensorless angle.

The invention relates to a method (400) for regulating an electric machine (190), comprising at least one first filter (140) and at least one second filter (142, 144). The method has the steps of: ascertaining (410) a feedback variable (Idq); filtering (412) a specifiable GW matching variable (Idq*); ascertaining (414) the filtered feedback variable without fundamental components (IdqWo-Funda); filtering (416) the filtered feedback variable without fundamental components (IdqWo-Funda); ascertaining (418) a filtered feedback variable without harmonic components (IdqFunda); and energizing (480) at least one winding of the electric machine (190) on the basis of the filtered feedback variable without harmonic components (IdqFunda).