145343 20260217205518.0 doi 10.3390/electronics13112208 sideral 140121 ART-2024-140121 eng de Curtò, J. Hybrid state estimation: integrating physics-informed neural networks with adaptive UKF for dynamic systems 2024 Access copy available to the general public Unrestricted In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains. info:eu-repo/grantAgreement/EC/H2020/101103983/EU/Next generation technologies for battery systems in transport electrification based on novel design approach to increase performance and reduce carbon footprint/NEXTBAT This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101103983-NEXTBAT info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.6 2024 ENGINEERING, ELECTRICAL & ELECTRONIC 174 / 368 = 0.473 2024 Q2 T2 PHYSICS, APPLIED 97 / 187 = 0.519 2024 Q3 T2 COMPUTER SCIENCE, INFORMATION SYSTEMS 136 / 258 = 0.527 2024 Q3 T2 0.615 2024 Computer Networks and Communications 2024 Q2 Control and Systems Engineering 2024 Q2 Signal Processing 2024 Q2 Hardware and Architecture 2024 Q2 Electrical and Electronic Engineering 2024 Q2 6.1 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Zarzà, I. de Universidad de Zaragoza (orcid)0000-0002-5844-7871 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 13, 11 (2024), 2208 [23 pp.] Electronics (Basel) Electronics (Basel) 2079-9292 1666280 http://zaguan.unizar.es/record/145343/files/texto_completo.pdf Versión publicada 2640356 http://zaguan.unizar.es/record/145343/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:145343 articulos driver 2026-02-17-20:27:37 ARTICLE