Design and implementation of the extended Kalman filter for the speed and rotor position estimation of brushless DC motor

Terzić, Božo; Jadrić, Martin

doi:10.1109/41.969385

Cited by 130 publications

(59 citation statements)

References 8 publications

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“…[3][4] Due to its performance of state estimation for 1-4244-2386-6/08/$20.00 ©2008 IEEE nonlinear systems, Kalman filter has been widely applied for the estimation of rotor position and speed in synchronous motor drives. [5]- [8] However in these papers this method is still based on the magnitude ofback-EMF voltage which is not suitable for low speed.…”

Section: Introductionmentioning

confidence: 97%

Sensorless control of permanent-magnet synchronous motor based on high-frequency signal injection and Kalman filter

Zhou

Wang

et al. 2008

2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics

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This paper presents a method for permanent magnet synchronous motor rotor position estimation at low speed with high accuracy. The high-frequency voltage signal is injected into motor stator windings, and the corresponding current signal containing information of rotor position estimation error is demodulated. A Kalman filter is chosen as the rotor position observer to depress the nonlinear effect caused by inverter and improve the estimation accuracy at low speed. Simulation results show that this method is applicable to both steady state and dynamic state estimation of rotor position. With the method proposed, a nsp (TMS320F2812) based position control system is implemented and tested. The experimental results are also presented in this paper, which show that the proposed scheme can achieve high accuracy at low speed.

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Section: Introductionmentioning

confidence: 97%

Sensorless control of permanent-magnet synchronous motor based on high-frequency signal injection and Kalman filter

Zhou

Wang

et al. 2008

2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics

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“…Rotor position and speed estimation of permanent magnet synchronous motors using EKF was established in [14] in which the measured signal is filtered for eliminating higher order harmonics. An EKF estimator for trapezoidal back EMF motor by using only the stator line voltages and currents is implemented in [15]. A suitable selection of process noise and measurement noise covariance matrices is important in EKF estimation.…”

Section: Introductionmentioning

confidence: 99%

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

Alex

Daniel

2018

Modelling and Simulation in Engineering

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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.

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“…Since the high precision position encoder and load torque sensor are expensive and have strict requirements of installation environment, many sensorless methods have been proposed to control the PMSM in recent years, for example the sliding mode observer [2][3] , the Neural Network control [4] and the Kalman filter [5][6] . However when the PMSM works in the low speed region, the back electromotive force is comparatively small, the sensorless PMSM fails to estimate the rotor position precisely by using the back electromotive force, and the speed signal with an encoder has high quantization noise as well.…”

Section: Introductionmentioning

confidence: 99%

Low speed control for PMSM based on reduced-order adaptive Kalman filter

Chen

Liu

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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This paper proposes a dual reduced-order adaptive Kalman observer to estimate the rotor speed and load torque of PMSM under low speed condition. The observed speed is more precise than encoder differential signals which has more noise, and the estimated torque can be used as feedforward compensation to reduce speed ripple when load changes. Because the calculation amount of Kalman filter is proportional to its order's cube, a dual reduced-order observer is proposed. To reduce the influence of modeling errors, adaptive algorithm with forgetting factor is proposed to calculate the noise covariance. The simulation and experiment results show the good performance of the proposed strategy.

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Design and implementation of the extended Kalman filter for the speed and rotor position estimation of brushless DC motor

Cited by 130 publications

References 8 publications

Sensorless control of permanent-magnet synchronous motor based on high-frequency signal injection and Kalman filter

Sensorless control of permanent-magnet synchronous motor based on high-frequency signal injection and Kalman filter

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

Low speed control for PMSM based on reduced-order adaptive Kalman filter

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