PMSM speed and position estimation method based on parameter adaptive EKF

Abstract

The invention relates to a PMSM (permanent magnet synchronous motor) speed and position estimation method based on a parameter self-adaptive EKF (extended Kalman filter), which realizes double-closed-loop control of a current loop and a speed loop of a motor according to an SVPWM (space vector pulse width modulation) theory and a method for controlling a motor vector. Ualpha, ubeta and ialpha and ibeta under an alpha-beta static coordinate system are obtained through FOC control and serve as input of an extended Kalman filtering algorithm, meanwhile, phase resistance, phase inductance and flux linkage of the motor need to serve as input parameters, and the rotor rotating speed we and the rotor position theta are obtained through extended Kalman recursion. Compared with traditional extended Kalman filtering, an accurate process noise variance Q must exist when a priori estimation covariance is obtained, the improved method provided by the invention is used for obtaining the priori estimation covariance based on a posteriori estimation information sequence as feedback, and the problem of dependence on the process noise covariance Q is solved.

Description

technical field [0001] The invention relates to a parameter adaptive EKF-based PMSM speed and position estimation method, belonging to the technical field of permanent magnet synchronous motors. Background technique [0002] In the past few decades, the development of modern industry has made the types of motors more and more abundant. Among them, permanent magnet synchronous motors are widely used in traditional equipment and CNC machine tools, due to their simple structure, low loss, and high reliability. Emerging fields such as robotics. [0003] At present, the control of permanent magnet synchronous motor is divided into sensory and non-sensory control, but sensory control will increase the volume and cost of the motor, which limits the application in some special occasions, so there is non-sensory control, sensorless control technology At present, there are more model reference adaptive (MRAS), extended Kalman filter (EKF), and synovium observers. Among them, the meth...

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Problems solved by technology

[0003] At present, the control of permanent magnet synchronous motor is divided into sensory and non-sensory control, but sensory control will increase the volume and cost of the motor, which limits the application in some special occasions, so there is non-sensory control, sensorless control technology At present, there are more model reference adaptive (MRAS), extended Kalman filter (EKF), and synovium observers. Among them, the method based on extended Kalman filter depends on the precondition that the system model and noise parameters are known. , relying on an accurate priori known noise parameter Q, using a wrong or biased noise parameter Q will lead to a decrease in filter estimation accuracy, which in turn will affect the final velocity and position estimation

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