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

Mohamed Ahmeid, Matthew Armstrong, Shady Gadoue, Maher Al-Greer

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalIEEE Transactions on Power Electronics
Early online date1 Nov 2017
DOIs
Publication statusE-pub ahead of print - 1 Nov 2017

Bibliographical note

© Copyright 2017 IEEE - All rights reserved.

Keywords

  • System Identification
  • Switch Mode Power Converters
  • Digital Control
  • Parametric Estimation
  • Kalman Filter
  • Self-tuning Controller

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