Oscar G. Ibarra-Manzano, José A. Andrade-Lucio, Yuriy S. Shmaliy, "Gain Computation for Batch $$H_{2}$$-FIR Filtering of Predictive Uncertain Disturbed Models using LMI," WSEAS Transactions on Systems, vol. 24, pp. 112-119, 2025, DOI:10.37394/23202.2025.24.13
Oscar G. Ibarra-Manzano, José A. Andrade-Lucio, Yuriy S. Shmaliy. Gain Computation for Batch $$H_{2}$$-FIR Filtering of Predictive Uncertain Disturbed Models using LMI.
WSEAS Transactions on Systems. 2025;24:112-119. 10.37394/23202.2025.24.13
WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 24, 2025
Gain Computation for Batch $$H_{2}$$-FIR Filtering of Predictive Uncertain Disturbed Models using LMI
Authors: , ,
Abstract: The gain for the receding horizon (RH) $$H_{2}$$ finite impulse response (FIR) filter is derived using linear
matrix inequality (LMI) under uncertainties, disturbances, initial, and measurement errors. The RH $$H_{2}$$-FIR filter
is developed by minimizing the squared Frobenius norm of the weighted error-to-error transfer function, where
the weights are related to errors. The filter is tested by a harmonic model with an uncertain system matrix, and its
higher accuracy is shown against the OFIR, Kalman, maximum likelihood FIR, and unbiased FIR (UFIR) filters.
Keywords:
RH $$H_{2}$$-FIR filter, OFIR filter, UFIR filter, Kalman filter, uncertainty, disturbance, robustness
Pages: 112-119 WSEAS Transactions on Systems, ISSN / E-ISSN: 1109-2777 / 2224-2678, Volume 24, 2025, Art. #13 DOI: 10.37394/23202.2025.24.13
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