Real-time system identification for load monitoring and transient handling of Dc-Dc supplies

Pitel, Grant; Krein, P.T.

doi:10.1109/pesc.2008.4592548

Cited by 25 publications

(28 citation statements)

References 10 publications

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“…To justify the identificatio results, the discrete transfer function of the average model in (11) is calculated in advance, at a sampling time of 50 μs. In line with many other sources of literature, convergence time and accuracy are considered to be the important metrics in evaluating the adaptive algorithm performance [2], [4] …”

Section: Simulation Resultsmentioning

confidence: 98%

“…Here, the values of coefficient a and b are dependent on the Laplace transfer function coefficient define in (1), and on the digital sampling time, T [2], [4].…”

Section: A Discrete Time Modelingmentioning

confidence: 99%

“…as sudden load variations, components aging, noise, and unpredictable changes in operating mode may degrade the controller performance and can lead to instability within the entire system [3], [4]. For these reasons, adaptive and autotuning controllers, based on the system identificatio of the converter parameters, are now gaining more attention.…”

mentioning

confidence: 99%

“…The second paradigm, parametric system identification assumes a known model structure with prespecifie order and number of coefficient to be estimated [5]. According to the literature, conventional least squares [5], [13] and its recursive version, recursive least squares (RLS) [4], [14], [15], are the most commonly used algorithms for parameter estimation of dc-dc converters. In [4], the classical RLS algorithm is reviewed and tested in real time on an open-loop buck converter.…”

mentioning

confidence: 99%

“…According to the literature, conventional least squares [5], [13] and its recursive version, recursive least squares (RLS) [4], [14], [15], are the most commonly used algorithms for parameter estimation of dc-dc converters. In [4], the classical RLS algorithm is reviewed and tested in real time on an open-loop buck converter. It is confirme that the classical RLS algorithm can result in accurate parameter estimation for systems with fi ed, or slow varying, loads while operating at sampling frequency much lower than the switching frequency.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Real-Time Parameter Estimation of DC–DC Converters Using a Self-Tuned Kalman Filter

Ahmeid

Armstrong

Gadoue

et al. 2017

IEEE Trans. Power Electron.

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Abstract-To achieve high-performance control of modern dcdc 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.

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Section: Simulation Resultsmentioning

confidence: 98%

“…Here, the values of coefficient a and b are dependent on the Laplace transfer function coefficient define in (1), and on the digital sampling time, T [2], [4].…”

Section: A Discrete Time Modelingmentioning

confidence: 99%

mentioning

confidence: 99%