Journal
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Volume -, Issue -, Pages -Publisher
WILEY
DOI: 10.1002/rnc.6347
Keywords
H-2-FIR state observer; Kalman filter; optimal unbiased filter; prediction; receding horizon; robustness
Funding
- Secretaria de Educacion Publica (SEP) [A1-S-10287, CB2017-2018]
- Consejo Nacional de Ciencia y Tecnologia
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In this article, a robust H-2 optimal unbiased FIR predictor and filter are developed for uncertain and disturbed systems, demonstrating superior performance over existing filters under severe disturbances and large timing error.
In this article, we develop a robust H-2 optimal unbiased FIR (H-2-OUFIR) predictor and receding horizon (RH) H-2-OUFIR filter for uncertain and disturbed systems in the presence of measurement errors. The state observers are designed using a novel H-2-FIR approach by minimizing the squared Frobenius norm of the weighted error-to-error transfer function, where weights are related to errors. The bias-constrained RH H-2-FIR filtering algorithm using the linear matrix inequality is also obtained. The RH H-2-OUFIR filter is applied to uncertain and disturbed quasi-periodic harmonic systems, where the uncertainty is due to random sampling and the disturbance is Gauss-Markov. It is shown that the RH H-2-OUFIR filter outperforms in terms of accuracy and robustness the Kalman, OFIR, UFIR, and maximum likelihood FIR filters under sever disturbances and large timing error.
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