Position Sensorless Control of Interleaved CSI Fed PMSM Drive With Extended Kalman Filter

2012 ◽  
Vol 48 (11) ◽  
pp. 3688-3691 ◽  
Author(s):  
Zheng Wang ◽  
Yang Zheng ◽  
Zhixiang Zou ◽  
Ming Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
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.

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