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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Purnawan, Heri
Department of Electrical Engineering, Universitas Islam Lamongan, Lamongan, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Uncertainty-Aware Kalman Filtering via Intrusive Polynomial Chaos for Disturbance Estimation Purnawan, Heri; Wakhid, Abdur Rohman; Fiddina, Qori Afiata; Iza, Belgis Ainatul; Sanusi, Tri Muhamad; Cahyaningtias, Sari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26488

Abstract

Robust control under parameter uncertainty requires reliable disturbance estimation. This paper proposes an uncertainty-aware method, namely Intrusive Polynomial Chaos-based Kalman Filter (IPC-KF) for systems with probabilistic parameters and measurement noise. The method is evaluated through two numerical case studies and compared with a nominal Kalman filter (KF). Results from 100 realizations, assessed using RMSE and mean variance, show that the IPC-KF achieves estimation accuracy comparable to the nominal KF. For the spring-mass-damper system, the RMSE difference is below , with both methods yielding the same mean variance of . For the F-16 aircraft model, identical RMSE values and a mean variance of are obtained. While IPC-KF captures parameter uncertainty via polynomial chaos, augmenting the state with disturbances does not necessarily improve estimation accuracy. Further studies are needed to assess uncertainty bounds and robustness.
Co-Authors Cahyaningtias, Sari Fiddina, Qori Afiata Iza, Belgis Ainatul Sanusi, Tri Muhamad Wakhid, Abdur Rohman