Physics-Infused Neural MPC of a DC-DC Boost Converter with Adaptive Transient Recovery and Enhanced Dynamic Stability

TL;DR

This paper introduces a hybrid physics-informed neural network combined with finite control set MPC for boost converters, achieving improved transient response, stability, and efficiency in real-time power electronics control.

Contribution

It presents a novel hybrid control framework that integrates physics-informed neural networks with MPC, enhancing stability and transient recovery in boost converters.

Findings

Improved transient response and voltage ripple reduction.

Robust operation across different conduction modes.

Computationally efficient and physically consistent control.

Abstract

DC-DC boost converters require advanced control to ensure efficiency and stability under varying loads. Traditional model predictive control (MPC) and data-driven neural network methods face challenges such as high complexity and limited physical constraint enforcement. This paper proposes a hybrid physics-informed neural network (PINN) combined with finite control set MPC (FCS-MPC) for boost converters. The PINN embeds physical laws into neural training, providing accurate state predictions, while FCS-MPC ensures constraint satisfaction and multi-objective optimization. The method features adaptive transient recovery, explicit duty-ratio control, and enhanced dynamic stability. Experimental results on a commercial boost module demonstrate improved transient response, reduced voltage ripple, and robust operation across conduction modes. The proposed framework offers a computationally efficient, physically consistent solution for real-time control in power electronics.