FPGA sensorless PMSM drive with adaptive fading extended Kalman filtering

Quang, NK; Ha, QP; Hieu, NT

FPGA sensorless PMSM drive with adaptive fading extended Kalman filtering

Quang, NK Ha, QP

Hieu, NT

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Publication Type:: Conference Proceeding
Citation:: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, pp. 295 - 300
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Quang, NK	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Hieu, NT	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, pp. 295 - 300	en_US
dc.identifier.isbn	9781479951994	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31068
dc.identifier.uri	http://hdl.handle.net/10453/31069
dc.identifier.uri	http://hdl.handle.net/10453/31065
dc.description.abstract	© 2014 IEEE. This paper addresses the design and implementation of an Adaptive Fading Extended Kaiman Filter (AF-EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the implemented AF-EKF and their estimates are then used in vector control of the PMSM. In conventional Kaiman filtering, abrupt state changes may not be tracked adequately since sudden variations may seriously affect the auto-correlation Gaussian property of white noise in the filter residuals. For this, the AF-EKF has been developed to recover the estimation results in events of frequent and sharp state jumps. The AF-EKF is, therefore, a promising estimator for PMSM drives that are subject to frequently-varying loads speed commands. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high-speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows effectiveness of the proposed chip-based AF-EKF PMSM speed estimation.	en_US
dc.relation.ispartof	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014	en_US
dc.relation.isbasedon	10.1109/ICARCV.2014.7064321	en_US
dc.title	FPGA sensorless PMSM drive with adaptive fading extended Kalman filtering	en_US
dc.type	Conference Proceeding
utslib.for	090506 Structural Engineering	en_US
utslib.for	1099 Other Technology	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. This paper addresses the design and implementation of an Adaptive Fading Extended Kaiman Filter (AF-EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the implemented AF-EKF and their estimates are then used in vector control of the PMSM. In conventional Kaiman filtering, abrupt state changes may not be tracked adequately since sudden variations may seriously affect the auto-correlation Gaussian property of white noise in the filter residuals. For this, the AF-EKF has been developed to recover the estimation results in events of frequent and sharp state jumps. The AF-EKF is, therefore, a promising estimator for PMSM drives that are subject to frequently-varying loads speed commands. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high-speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows effectiveness of the proposed chip-based AF-EKF PMSM speed estimation.

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http://hdl.handle.net/10453/31065