scholarly journals An Efficient Position Tracking Smoothing Algorithm for Sensorless Operation of Brushless DC Motor Drives

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Surya Susan Alex ◽  
Asha Elizabeth Daniel
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Accuracy ◽  
Smoothing Algorithm ◽  
State Variables ◽  
Present Measurement ◽  
Brushless Dc Motors ◽  
Rotor Position ◽  
Brushless Dc ◽  
Dc Motors

This paper presents a new position sensorless scheme in which a smoothing filter algorithm is proposed to improve the results obtained through Extended Kalman Filter (EKF) algorithm in tracking the rotor position for sensorless control of brushless DC motors. The rotor position and speed are estimated from the input voltage and current using the Extended Kalman Filter. States obtained through filtering in each sampling instant are refined, using the new smoothing algorithm, giving much better results. In the proposed method, the estimated state in previous instant is enhanced using the present measurement sample by the smoothing algorithm which is then used to improve the present estimated state variables. The complete system is modelled and simulated in MATLAB to verify the merit of the proposed smoothing algorithm. A comparison with conventional EKF is done for various load torque and speed conditions to establish the performance of the new sensorless algorithm. Simulation results show that the proposed smoothing technique offers better estimation accuracy. The peak error in the estimated speed and rotor position is considerably reduced when compared with EKF. The improved state estimate can be used as feedback for speed control of brushless DC motors in variable speed drives.

scholarly journals On Stability of Open-Loop Operation without Rotor Information for Brushless DC Motors

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhong Wu ◽  
Haotun Lyu ◽  
Yongli Shi ◽  
Di Shi
Keyword(s):  
Theoretical Analysis ◽  
Operation Mode ◽  
Open Loop ◽  
Control Mode ◽  
Open Loop Control ◽  
Brushless Dc Motors ◽  
Loop Control ◽  
Rotor Position ◽  
Brushless Dc ◽  
Dc Motors

Open-loop operation mode is often used to control the Brushless DC Motors (BLDCMs) without rotor position sensors when the back electromotive force (EMF) is too weak due to the very low rotor velocity. The rotor position information is not necessary in this mode and the stator windings are supplied with voltages under a certain ratio of the amplitude to the frequency. However, the rotor synchronization will be destroyed once if the commutation instant is inappropriate. In order to improve the reliability of the open-loop operation mode, a dynamic equation is established to represent the synchronization error between the rotor and the stator. Thereafter, the stability of the open-loop control mode is analyzed by using Lyapunov indirect method. Theoretical analysis indicates that the open-loop control mode is asymptotically stable only when the commutation instant of the stator current lags behind the ideal one suitably. Finally, theoretical analysis is verified through the experimental results of a certain BLDCM.

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 Derivative‐free square‐root cubature Kalman filter for non‐linear brushless DC motors

2016 ◽  
Vol 10 (5) ◽  
pp. 419-429 ◽  
Author(s):  
Devendra Potnuru ◽  
Kumar Pakki Bharani Chandra ◽  
Ienkaran Arasaratnam ◽  
Da‐Wei Gu ◽  
Karlapudy Alice Mary ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Cubature Kalman Filter ◽  
Derivative Free ◽  
Non Linear

scholarly journals Initial Rotor Position Detection for Brushless DC Motors Based on Coupling Injection of High-Frequency Signal

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 133433-133441 ◽  
Author(s):  
Wei Chen ◽  
Shuhai Dong ◽  
Xinmin Li ◽  
Yanfei Cao ◽  
Guozheng Zhang
Keyword(s):  
High Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Brushless Dc Motors ◽  
Rotor Position ◽  
Brushless Dc ◽  
Dc Motors ◽  
Position Detection

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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