Data-Driven Control Of Power Converters

TL;DR

This work tackles data-driven PI control of a buck DC-DC converter using VRFT, avoiding explicit modeling by synthesizing a controller directly from input-output data. It compares Ziegler-Nichols tuning with VRFT, demonstrating that VRFT yields faster response but can suffer undershoot due to duty-cycle saturation. By adding an anti-windup extension, VRFT further improves tracking accuracy and reduces undershoot and settling time. The results are simulation-based on an OwnTech model, highlighting practical benefits and pointing to future experimental validation and exploration under more dynamic operating conditions.

Abstract

The fundamental role of power converters is to efficiently manage and control the flow of electrical energy, ensuring compatibility between power sources and loads. All these applications of power converters need the design of an appropriate control law. Control of power converters is a challenging problem due to the presence of switching devices which are difficult to handle using traditional control approaches. The objective of this paper is to investigate the use of data-driven techniques, in particular the Virtual References Feedback Tuning (VRFT) method, in the context of power converters feedback control. This study considers a buck \pauline{mode} power converter circuit provided by the OwnTech foundation.

Figures (14)

• Figure 1: OwnTech's converter circuit.
• Figure 2: Considered closed-loop control structure.
• Figure 3: OwnTech's power converter.
• Figure 4: Comparison between SSA model and Power converter
• Figure 5: Principle of VRFTref5