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

Abstract

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a DC-DC switch-mode power converter (SMPC). The proposed estimation algorithm is based on a novel combination between the classical Kalman filter and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the Kalman Filter (KF), with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky PID controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a pre-calculated average model.

Publication DOI: https://doi.org/10.1109/TPEL.2017.2768618
Divisions: Engineering & Applied Sciences
Additional Information: © Copyright 2017 IEEE - All rights reserved.
Uncontrolled Keywords: System Identification,Switch Mode Power Converters,Digital Control,Parametric Estimation,Kalman Filter,Self-tuning Controller
Full Text Link: http://ieeexplo ... cument/8093696/
Related URLs:
PURE Output Type: Article
Published Date: 2017-11-01
Published Online Date: 2017-11-01
Accepted Date: 2017-11-01
Authors: Ahmeid, Mohamed
Armstrong, Matthew
Gadoue, Shady
Al-Greer, Maher

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