Motor rotor position estimation method based on exponential fitting volume Kalman filtering

Abstract

The invention discloses a permanent magnet synchronous motor rotor position estimation method based on exponential fitting cubature Kalman filter (ECKF), which can be applied to the technical field of permanent magnet synchronous motor sensorless rotor position estimation and solves the problem of low rotor position estimation precision in the prior art. The invention provides an exponential fitting cubature Kalman filtering method which performs optimal approximation on an integral part of a product of a nonlinear function and a Gaussian probability density function in a cubature Kalman filtering process through an exponential fitting method, and combines a sphere radial numerical approximation rule in a Gaussian filtering method to realize abetter fitting effect on a nonlinear system. The method is used for estimating the position of a rotor of a surface-mounted permanent magnet synchronous motor, so that the estimated position of the rotor of the motor is more accurate, and the control effect is better.

Description

technical field [0001] The invention relates to the application of a built-in permanent magnet synchronous motor, for example, in the fields of energy vehicles, transport aircraft and the like, and in particular to the exponential fitting of a surface-mounted permanent magnet synchronous motor with higher accuracy in the control of non-inductive sensors. Volumetric Kalman Filter (ECKF) Rotor Position Estimation Method. Background technique [0002] At present, the methods of estimating rotor position mainly include model reference adaptive method, state observer estimation method, artificial intelligence method, etc. Most of the estimation methods need to use accurate motor parameters for estimation. PMSM is a complex object with multiple variables, strong coupling, nonlinearity and variable parameters, and in practice PMSM changes with the operating conditions. Compared with the traditional control, the rotor position estimation in the sliding mode observer algorithm (SMO)...

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Problems solved by technology

Compared with the traditional control, the estimation of the rotor position by the sliding mode observer algorithm (SMO) and the extended Kalman filter (EKF) algorithm does not have high requirements for the accuracy of the mathematical model. However, due to the traditional sliding mode control system in the sliding mode state With the high-frequency fighting array, the estimated back EMF will always have high-frequency chattering phenomenon, and the extended Kalman filter linearization fit is not strong, and his linearization is based on the derivative method to locally linearize the nonlinearity System model, the linear approximation of the nonlinear model leads to problems such as poor estimation accuracy and high divergence rate, so it has been a bottleneck of sensorless control

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