Estimation and control of sensorless brushless DC motor drive using Extended Kalman Filter

2016 ◽  
Author(s):  
V. Aishwarya ◽  
B. Jayanand
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Motor Drive ◽  
Brushless Dc ◽  
Estimation And Control ◽  
And Control

EXTENDED KALMAN FILTER BASED BRUSHLESS DC MOTOR FOR ROTOR POSITION AND SPEED CONTROL

2021 ◽  
Author(s):  
Nabiya Ellahi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Reference Signal ◽  
Dc Motor ◽  
Position Estimation ◽  
Brushless Dc Motor ◽  
Zero Crossing ◽  
Rotor Position ◽  
Brushless Dc ◽  
Improved Performance

A method to control speed and rotor position with improved performance has been described in this research. Various techniques are taken into consideration with their detailed description. During this process new methods are also introduced with their pros and cons. The research includes a detailed study of progressive back-Emf sensing strategies. The relevant methods, which can support estimation, are the back Emf zero-crossing method, integration of voltage, and position estimation by flux and inductance. In this thesis, Extended Kalman filter is utilized for position and speed estimation. Firstly, DC voltage will be applied as an input. Extended Kalman Filter is used to perform state estimation while PID controller is employed to regulate the system state following the reference signal. The proposed solution leads to control of the ripple generated in speed and torque of Brushless DC Motor and improved performance.

scholarly journals EXTENDED KALMAN FILTER BASED BRUSHLESS DC MOTOR FOR ROTOR POSITION AND SPEED CONTROL

2021 ◽  
Author(s):  
Nabiya Ellahi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Reference Signal ◽  
Dc Motor ◽  
Position Estimation ◽  
Brushless Dc Motor ◽  
Zero Crossing ◽  
Rotor Position ◽  
Brushless Dc ◽  
Improved Performance

A method to control speed and rotor position with improved performance has been described in this research. Various techniques are taken into consideration with their detailed description. During this process new methods are also introduced with their pros and cons. The research includes a detailed study of progressive back-Emf sensing strategies. The relevant methods, which can support estimation, are the back Emf zero-crossing method, integration of voltage, and position estimation by flux and inductance. In this thesis, Extended Kalman filter is utilized for position and speed estimation. Firstly, DC voltage will be applied as an input. Extended Kalman Filter is used to perform state estimation while PID controller is employed to regulate the system state following the reference signal. The proposed solution leads to control of the ripple generated in speed and torque of Brushless DC Motor and improved performance.

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