Real-Time Parameter Estimation of DC–DC Converters Using a Self-Tuned Kalman Filter
| Autor: | Maher Al-Greer, Petros Missailidis, Mohamed Ahmeid, Shady Gadoue, Matthew Armstrong |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry Estimation theory Buck converter 020209 energy 020208 electrical & electronic engineering System identification Estimator 02 engineering and technology Kalman filter Converters Power (physics) Microcontroller Control theory 0202 electrical engineering electronic engineering information engineering Electronic engineering Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Power Electronics. 32:5666-5674 |
| ISSN: | 1941-0107 0885-8993 |
| DOI: | 10.1109/tpel.2016.2606417 |
| Popis: | 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 microcontroller 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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