Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter

Muhammad Rif'an; Feri Yusivar; Benyamin Kusumo Putro

doi:10.1145/3036331.3036353

Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter

Muhammad Rif'an, Feri Yusivar, Benyamin Kusumo Putro

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, a technique for estimation of the rotor speed and position of a BLDC motor is presented. BLDC motors are nonlinear system and to control BLDC, rotor position must be known. This is usually done by using sensors that result in increased costs, the size of the motor and reduces reliability. In this research, the rotor speed and rotor position of BLDC motor are estimated by Ensemble Kalman Filter only use the stator line voltage and current measurement. The Ensemble Kalman Filter (EnKF) is a recursive filter suitable for non-linear systems. The estimated rotor speed and position rotor has been used for the motor drive and closed loop speed control of the BLDC motor. The performance of the proposed technique is demonstrated through real-Time simulation.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Publisher	Association for Computing Machinery
Pages	192-195
Number of pages	4
ISBN (Electronic)	9781450348164
DOIs	https://doi.org/10.1145/3036331.3036353
Publication status	Published - 20 Jan 2017
Event	8th International Conference on Computer Modeling and Simulation, ICCMS 2017 - Canberra, Australia Duration: 20 Jan 2017 → 23 Jan 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128047

Conference

Conference	8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Country/Territory	Australia
City	Canberra
Period	20/01/17 → 23/01/17

Keywords

BLDC motor
Ensemble kalman filter
Estimation
Mathematical modeling

Access to Document

10.1145/3036331.3036353

Cite this

Rif'an, M., Yusivar, F., & Putro, B. K. (2017). Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter. In Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017 (pp. 192-195). (ACM International Conference Proceeding Series; Vol. Part F128047). Association for Computing Machinery. https://doi.org/10.1145/3036331.3036353

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title = "Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter",

abstract = "In this paper, a technique for estimation of the rotor speed and position of a BLDC motor is presented. BLDC motors are nonlinear system and to control BLDC, rotor position must be known. This is usually done by using sensors that result in increased costs, the size of the motor and reduces reliability. In this research, the rotor speed and rotor position of BLDC motor are estimated by Ensemble Kalman Filter only use the stator line voltage and current measurement. The Ensemble Kalman Filter (EnKF) is a recursive filter suitable for non-linear systems. The estimated rotor speed and position rotor has been used for the motor drive and closed loop speed control of the BLDC motor. The performance of the proposed technique is demonstrated through real-Time simulation.",

keywords = "BLDC motor, Ensemble kalman filter, Estimation, Mathematical modeling",

author = "Muhammad Rif'an and Feri Yusivar and Putro, {Benyamin Kusumo}",

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Rif'an, M, Yusivar, F & Putro, BK 2017, Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter. in Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. ACM International Conference Proceeding Series, vol. Part F128047, Association for Computing Machinery, pp. 192-195, 8th International Conference on Computer Modeling and Simulation, ICCMS 2017, Canberra, Australia, 20/01/17. https://doi.org/10.1145/3036331.3036353

Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter. / Rif'an, Muhammad; Yusivar, Feri ; Putro, Benyamin Kusumo.
Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. Association for Computing Machinery, 2017. p. 192-195 (ACM International Conference Proceeding Series; Vol. Part F128047).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Putro, Benyamin Kusumo

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N2 - In this paper, a technique for estimation of the rotor speed and position of a BLDC motor is presented. BLDC motors are nonlinear system and to control BLDC, rotor position must be known. This is usually done by using sensors that result in increased costs, the size of the motor and reduces reliability. In this research, the rotor speed and rotor position of BLDC motor are estimated by Ensemble Kalman Filter only use the stator line voltage and current measurement. The Ensemble Kalman Filter (EnKF) is a recursive filter suitable for non-linear systems. The estimated rotor speed and position rotor has been used for the motor drive and closed loop speed control of the BLDC motor. The performance of the proposed technique is demonstrated through real-Time simulation.

AB - In this paper, a technique for estimation of the rotor speed and position of a BLDC motor is presented. BLDC motors are nonlinear system and to control BLDC, rotor position must be known. This is usually done by using sensors that result in increased costs, the size of the motor and reduces reliability. In this research, the rotor speed and rotor position of BLDC motor are estimated by Ensemble Kalman Filter only use the stator line voltage and current measurement. The Ensemble Kalman Filter (EnKF) is a recursive filter suitable for non-linear systems. The estimated rotor speed and position rotor has been used for the motor drive and closed loop speed control of the BLDC motor. The performance of the proposed technique is demonstrated through real-Time simulation.

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KW - Mathematical modeling

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Estimation and control of sensorless brushless dc motor drive using ensemble kalman filter

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