Uncertainty-Aware Kalman Filtering via Intrusive Polynomial Chaos for Disturbance Estimation - Heri Purnawan; Abdur Rohman Wakhid; Qori Afiata Fiddina; Belgis Ainatul Iza; Tri Muhamad Sanusi; Sari Cahyaningtias (ZERO: Jurnal Sains, Matematika dan Terapan, 2025)
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.