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

Toimittaja(t)

Elsevier
2026
Artikkeli
SÀhkötekniikka|SÀhkötekniikka|Tietoliikennetekniik
vertaisarvioitu
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PysyvÀ osoite

https://urn.fi/URN:NBN:fi-fe2026021613756

Kuvaus

© 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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, đ¶) 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, đ¶), (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.

URI

https://doi.org/10.1016/j.epsr.2025.112671
https://urn.fi/URN:NBN:fi-fe2026021613756

Emojulkaisu

ISBN

ISSN

1873-2046
0378-7796

Aihealue

Kausijulkaisu

Electric power systems research|254

OKM-julkaisutyyppi

A1 AlkuperÀisartikkeli tieteellisessÀ aikakauslehdessÀ (vertaisarvioitu)

Avainsanat

Constant power loads (CPLs)Piecewise constant power loadsDC-DC buck convertersContinuous conduction mode (CCM)Real-time parameter estimationPhysics-informed neural networksModel predictive control (MPC)
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