Centralized System Identification of Multi-Rail Power Converter Systems Using an Iterative Decimation Approach

Jin Xu, Matthew Armstrong, Maher Al-Greer

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
101 Downloads (Pure)


This paper presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multi-rail power converters. The proposed approach uses an adaptive update rate as opposed to the fixed update rate used in conventional adaptive filters. Also, the step size/forgetting factors vary at different iteration stages. As a result, a reduced computational burden and faster model update can be achieved. Besides, recursive algorithms, such as Recursive Least Square (RLS), Fast Affine Projection (FAP) and Kalman Filter (KF), contain two important updates per iteration cycle; Covariance Matrix Approximation (CMA) update and Gradient Vector (GV) update. Usually, the CMA update requires the greater computational effort than the GV update. Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the CMA, calculated from the last iteration cycle for the current update can result in computational cost savings for real-time system identification. In this paper, both iteration rate adjustment and CMA re-cycling are combined and applied to simultaneously identify the power converter models in a three-rail power conversion architecture.
Original languageEnglish
Article number9440795
Pages (from-to)3520-3533
Number of pages14
JournalIEEE Transactions on Circuits and Systems
Issue number8
Publication statusPublished - 25 May 2021

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