Low-Complexity State-Space-Based System Identification and Controller Auto-Tuning Method for Multi-Phase DC–DC Converters

Abstract

The importance of online system identification (SI) in power electronics is ever increasing. It enables the tracking of system parameters, which in turn can be used for online controller tuning. Hence, SI is a key element for improving a converter's dynamic performance, stability, reliability. In this paper, a novel state-space-based SI approach utilizing the step-adaptive approximate least squares estimation algorithm with observation matrix randomization is proposed. The presented concept yields an accurate state-space model of the converter while simultaneously achieving a fast convergence rate and low computational complexity. Consequently, the estimated state-space model is utilized to automatically tune a full state feedback controller. This results in an improved converter performance in terms of overshoots, undershoots, and settling times. The proposed concept is verified by a prototype system comprising a two-phase buck converter and a field-programmable gate array. The provided measurement results highlight the effectiveness and benefits of the presented method over the state-of-the-art algorithms, as well as $z$ -domain estimation. It is shown that the number of required estimation iterations is more than halved in comparison with the state-of-the-art parametric SI approaches, while accuracy is improved.