This paper presents a decimation approach to significantly alleviate the computational burden of general estimation algorithms, such as the Recursive Least Square (RLS), Affine Projection (AP) and Kalman Filter (KF) methods. Unlike conventional iteration processes, in which the estimation update occurs after every sampling event, the proposed approach employs an adjustable update rate, rather than the conventional fixed rate. As a result, lower computational burden and faster system identification techniques can be achieved. In this paper, the technique is applied to both a single DC-DC switch mode power converter and, to demonstrate the applicability in complex systems, a multi-rail power converter architecture. Simulation results shows the effectiveness of the proposed algorithm.
|Publication status||Published - 25 Jun 2018|
|Event||IEEE 19th Workshop on Control and Modeling for Power Electronics - University of Padova, Padua, Italy|
Duration: 25 Jun 2018 → 28 Jun 2018
|Conference||IEEE 19th Workshop on Control and Modeling for Power Electronics|
|Period||25/06/18 → 28/06/18|