Model-free adaptive control for ultracapacitor based three-phase interleaved bidirectional DC-DC converter

The lack of an accurate mathematical model of non-linear systems, such as bidirectional DC-DC converters, brings certain limits to design a formidable controller for such systems. This article proposes a model-free adaptive control (MFAC) approach for an ultracapacitor based three-phase interleaved bidirectional DC-DC converter (BDC) to ameliorate the controller design limitations due to the demand for system model information. This article designs a pseudo gradient (PG) estimation algorithm based on the input and output (I/O) data of a three-phase interleaved BDC and designs a control input estimation algorithm through the PG estimation value, then, the design of a model-free adaptive controller is completed. Moreover, the Black-box technique is applied to the system, which does not require a precise mathematical model resulting in a lower computational burden, faster dynamics, and easier implementation. Furthermore, the feasibility and effectiveness of the proposed MFAC scheme are experimentally tested and verified on an energy storage system microgrid platform. This paper investigates a data-driven based model-free adaptive control approach for a three-phase interleaved bidirectional DC-DC converter (BDC) to improve the robustness and adaptability of the controllers. The suggested control scheme is only based on input-output (I/O) measurement data of three-phase interleaved bidirectional BDC, where any information about the system model is not required, leading to improved robustness against parameter drifts.image