000145343 001__ 145343
000145343 005__ 20241024135330.0
000145343 0247_ $$2doi$$a10.3390/electronics13112208
000145343 0248_ $$2sideral$$a140121
000145343 037__ $$aART-2024-140121
000145343 041__ $$aeng
000145343 100__ $$ade Curtò, J.
000145343 245__ $$aHybrid state estimation: integrating physics-informed neural networks with adaptive UKF for dynamic systems
000145343 260__ $$c2024
000145343 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145343 5203_ $$aIn 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.
000145343 536__ $$9info:eu-repo/grantAgreement/EC/H2020/101103983/EU/Next generation technologies for battery systems in transport electrification based on novel design approach to increase performance and reduce carbon footprint/NEXTBAT$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101103983-NEXTBAT
000145343 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145343 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145343 700__ $$0(orcid)0000-0002-5844-7871$$aZarzà, I. de$$uUniversidad de Zaragoza
000145343 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000145343 773__ $$g13, 11 (2024), 2208 [23 pp.]$$pElectronics (Basel)$$tElectronics$$x2079-9292
000145343 8564_ $$s1666280$$uhttps://zaguan.unizar.es/record/145343/files/texto_completo.pdf$$yVersión publicada
000145343 8564_ $$s2640356$$uhttps://zaguan.unizar.es/record/145343/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145343 909CO $$ooai:zaguan.unizar.es:145343$$particulos$$pdriver
000145343 951__ $$a2024-10-24-12:10:49
000145343 980__ $$aARTICLE