Online Identification of Permanent Magnet Flux Based on Extended Kalman Filter for IPMSM Drive With Position Sensorless Control

2012 ◽  
Vol 59 (11) ◽  
pp. 4169-4178 ◽  
Author(s):  
Yuchao Shi ◽  
Kai Sun ◽  
Lipei Huang ◽  
Yongdong Li
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Online Identification ◽  
Position Sensorless

Applications of Extended Kalman Filter for Position Sensorless Control of Permanent Magnet Linear Synchronous Motor

2013 ◽  
Vol 834-836 ◽  
pp. 1240-1245 ◽  
Author(s):  
Wei Min Yang ◽  
Li Jiao Pan ◽  
Peng Fei Zheng ◽  
Yong Qiang He
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Synchronous Motor ◽  
Position Sensor ◽  
Linear Synchronous Motor ◽  
Close Loop Control ◽  
Position Sensorless

Control system with position sensor is susceptible to the severe environment such as high temperature, humidity and vibration, which reduce the stability of control system. Position sensorless control of permanent magnet linear synchronous motor need not position sensor so that it can use in abominable environment. According to three phases voltage and two phases current measured from motor, the position and speed of motors mover can be estimated directly based on extended Kalman filter algorithm which is a kind of recursive algorithm. So position sensorless close loop control of PMLSM can be realized.

Position Sensorless Control Based on Augmented Extended Kalman Filter for Permanent Magnet Linear Synchronous Motor

2011 ◽  
Vol 88-89 ◽  
pp. 350-354
Author(s):  
Hua Cai Lu ◽  
Ming Jiang ◽  
Li Sheng Wei ◽  
Bing You Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Variable ◽  
Detailed Explanation ◽  
System States ◽  
Position Sensorless

In order to achieve position sensorless control for PMLSM drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter (AEKF) estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter (EKF). The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

scholarly journals Sensorless Control of Permanent Magnet Synchronous Motors and EKF Parameter Tuning Research

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Zhang ◽  
Xu-Feng Cheng
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Sensorless Control ◽  
Parameter Tuning ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Tuning Method

This paper concerns the parameter tuning and the estimated results postprocessing of the extended Kalman filter for the sensorless control application of permanent magnet synchronous motors. At first an extended Kalman filter parameter tuning method is proposed based on the theoretical and simulation analysis of extended Kalman filter parameters. Furthermore, a sensorless control system is proposed based on the parameter tuning method and the simulation analysis of extended Kalman filter estimation results in different reference speeds and different load torques. The proposed sensorless control system consists of two parts. The first one is a module to self-regulate extended Kalman filter parameters. The second part can correct the estimated speed and the estimated rotation angle based on the reference speed and the electromagnetic torque. Finally, simulation results are presented to verify the feasibility and validity of the proposed sensorless control system.

scholarly journals Implementation of Extended Kalman Filter with Optimized Execution Time for Sensorless Control of a PMSM Using ARM Cortex-M3 Microcontroller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3491
Author(s):  
Justas Dilys ◽  
Voitech Stankevič ◽  
Krzysztof Łuksza
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Execution Time ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Computation Time ◽  
Covariance Matrices ◽  
Kalman Gain ◽  
Novel Method

This paper addresses the implementation and optimization of an Extended Kalman Filter (EKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex-M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of EKF estimator was reduced from 260.4 μs to 37.7 μs without loss of accuracy. To further reduce EKF execution time, the separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update, a novel method was proposed, and the performance of it an EKF estimator with separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update was analyzed. Simulation and experiments results validate that the proposed technique could provide the same accuracy with less computation time. A tendency of minimum Kalman gain and covariance matrices calculation frequency from rotor electrical frequency was analyzed and are presented in the paper.

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