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

Abstract

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.

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

“…The Kalman Filter is a recursive method widely used to estimate unmeasured states and/or unknown parameters in a linear dynamic system [3,17]. In the KF configured for parameter estimation, the state-space model for parameter estimation problem is given by:…”

“…Adaptive and self-tuning controllers for switch mode power converter (SMPC) applications, based on system identification of the power converter parameters, are receiving increasing research attention [1][2][3]. This is in part due to the continuous fall in price, and improved processing performance of modern microprocessor platforms.…”

“…For this reason, the sampling frequency is selected to be much lower than the switching frequency in order to realise the proposed scheme on low-cost microcontrollers. In addition, the RLS estimation in real-time is highly affected by measurement noise and necessitates a sufficient level of perturbation to enhance the estimation accuracy and prevent estimator wind-up algorithm [3,11]. In [3], the Kalman filter (KF) configured for parameter estimation of SMPC is introduced.…”

“…In addition, the RLS estimation in real-time is highly affected by measurement noise and necessitates a sufficient level of perturbation to enhance the estimation accuracy and prevent estimator wind-up algorithm [3,11]. In [3], the Kalman filter (KF) configured for parameter estimation of SMPC is introduced. The simulation and real-time results show that the KF algorithm can handle the parameter estimation task efficiently with several advantages over the classical RLS adaptive algorithm.…”

“…In an SMPC system, this kind of prior knowledge can be acquired from modelling techniques such as the state space average model and by selecting an appropriate model structure. For this reason, in this paper the M-Max PU technique is adopted and developed to provide a computationally efficient parameter estimation scheme for a synchronous DC-DC buck converter based on the selftuned KF presented in [3]. Where: ( ) = ( )ĥ( ) and ( ) is measurement noise.…”