Unified dual-PINN solution for DC-DC power converter modeling and control with fast piecewise CPL sensing

Abstract

Constant-power loads (CPLs) impose strong nonlinearities on buck converters operating in continuous conduction mode (CCM), making stability and control highly sensitive to load power and passive parameters. This work proposes a three-stage inverse-to-forward physics-informed framework for online identification and predictive control. In the first stage, an inverse PINN (iPINN) jointly estimates the piecewise-constant CPL power and the passive parameters (𝐿, 𝐶) from voltage–current trajectories by enforcing averaged CCM dynamics and regularization terms, enabling reliable online identification during rapid transients. In the second stage, a direct PINN (DPINN) is trained using the identified parameters to construct a stable grey-box surrogate that embeds the buck conservation laws and generalizes across operating points. In the third stage, this physics-aware surrogate is integrated into a model predictive controller (MPC) to perform short-horizon duty-ratio optimization with accurate state forecasts. A key contribution of this work is a dual-PINN architecture—combining inverse estimation with forward physics-consistent prediction—that forms a unified identification–prediction–control pipeline. This integrated structure significantly reduces model–plant mismatch and enhances robustness to CPL steps and parameter drifts compared with conventional model-based or purely data-driven MPC schemes. Simulation results demonstrate (i) low-error parallel estimation of CPL and (𝐿, 𝐶), (ii) stable convergence of the forward surrogate, and (iii) improved closed-loop MPC performance, establishing a practical pathway from iPINN-based identification to real-time control.