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

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 and reliability. In this paper, a state-space based SI approach utilizing the step-adaptive least squares (SALS) 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 used to automatically tune a full state feedback (FSF) controller, resulting in an improved converter performance. A prototype system comprised of a two-phase buck converter and a field-programmable gate array (FPGA) is used to verify the proposed concept. The provided measurement results highlight the effectiveness and benefits of the presented method over state of the art z-domain estimation. It is shown that the number of required iterations is more than halved, while accuracy is improved.