Abstract
To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 micro-controller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method.
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume: 32
Issue: 7
Pages: 5666-5674
DOI: 10.1109/TPEL.2016.2606417
Published: JUL 2017
Reprint Address: Ahmeid, M (reprint author)
Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. |
Addresses:
[ 1 ] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England | |
[ 2 ] Alexandria Univ, Dept Elect Engn, Fac Engn, Alexandria 21544, Egypt |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Research Areas:Engineering
Web of Science Categories:Engineering, Electrical & Electronic
Document Type:Article
Language:English
Accession Number: WOS:000396134300054
ISSN: 0885-8993
eISSN: 1941-0107
IDS Number: EN6SS
Cited References in Web of Science Core Collection: 25
Times Cited in Web of Science Core Collection: 0