SSP Podded Propulsion Motor Control Based on SR-CDKF

2014 ◽  
Vol 986-987 ◽  
pp. 1142-1145
Author(s):  
Wen Long Yao
Keyword(s):  
Qr Decomposition ◽  
Estimation Accuracy ◽  
Square Root ◽  
Rotor Speed ◽  
Closed Loops ◽  
Speed Controller ◽  
Sensorless Vector Control ◽  
Matrix Square Root ◽  
The Matrix ◽  
Vector Control System

In this paper,the rotor speed and the position of the SSP propulsion motor are estimated for building sensorless vector control system with speed and current double closed loops based on square root center difference Kalman filter (SR-CDKF) algorithm. This method makes use of the QR decomposition linear algebra techniques and so on, and it updates the matrix square-root of the state covariance by the Cholesky factor updating. This method can not only get the more steady results but also improve the estimation accuracy of the SSP podded propulsion system. Simulation result shows that the improved CDKF algorithm is not only more accurate but also has higher rate of convergence compared with CDKF speed controller.

Application of Active Disturbance Rejection Cotroller in Sensorless Vector Control System of PMSM

2013 ◽  
Vol 273 ◽  
pp. 449-453
Author(s):  
Shu Gong Xue ◽  
Xin Zhu Sun
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Extended Kalman Filter ◽  
Disturbance Rejection ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Accuracy ◽  
Speed Controller ◽  
Sensorless Vector Control ◽  
Active Disturbance Rejection ◽  
Vector Control System

To improve the anti-disturbance capabilities of sensorless control system of permanent magnet synchronous motor PMSM), study on application of active disturbance rejection controller (ADRC) is put forward.The extended Kalman filter (EKF) algorithm is proprosed to estimate the rotor speed and position of PMSM for the realization of sensorless control.The ADRC is introduced to the control of speed loop to improve the control performances. Simulation results show that extended kalman filter algorithm worked well,and the proposed control strategy is superior to conventional PI speed controller for its no overshoot, high estimation accuracy,stronger capacity of anti-load-torque- disturbance and etc.

scholarly journals Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Rhudy ◽  
Yu Gu ◽  
Jason Gross ◽  
Marcello R. Napolitano
Keyword(s):  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Square Root ◽  
Fusion Algorithm ◽  
Matrix Square Root ◽  
Global Positioning ◽  
The Matrix ◽  
Aerial Vehicle ◽  
Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

A note on computing the matrix square root

CALCOLO ◽  
2003 ◽  
Vol 40 (4) ◽  
pp. 273-283 ◽  
Author(s):  
B. Iannazzo
Keyword(s):  
Square Root ◽  
Matrix Square Root ◽  
The Matrix

Speed Sensorless Vector Control System of Induction Motor

2012 ◽  
Vol 516-517 ◽  
pp. 1664-1667 ◽  
Author(s):  
Hong Yu Wang ◽  
Wen Long Cai ◽  
Cheng Wei Hou
Keyword(s):  
Control System ◽  
Vector Control ◽  
Induction Motor ◽  
Estimation Method ◽  
Identification System ◽  
Rotor Speed ◽  
Motor Speed ◽  
Speed Sensorless ◽  
Sensorless Vector Control ◽  
Vector Control System

This paper introduces a vector control system for speed sensorless induction motor drive, which we have recently developed. In the introduce vector control system, one induction motor’s rotor speed estimation method based on model reference adaptive identification theory is proposed. The induction motor speed identification system based on the proposed method can estimate the rotor speed of the induction motor. The speed sensorless vector control system based on proposed method in this paper was built with Simulink blocks in Matlab platform. The simulation results indicate that the proposed method could operate stably in whole range of speed with preferable identification precision of rotor speed.

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