The novel theory of robust a posteriori optimal finite impulse response (OFIR) filtering is
developed in linear discrete-time state-space for uncertain and disturbed systems under …

Receding horizon (RH) finite impulse response (FIR) filters (observers) are used in model
predictive and state feedback control. For such applications, we develop a robust RH …

In harsh environments, tracking is organized assuming disturbances, initial errors, and data
errors that requires robust algorithms. In this article, we develop, in discrete-time state space …

This article considers uncertain disturbed systems represented under initial and
measurement errors in linear discrete‐time state‐space. A novel bias‐constrained a …

This paper considers uncertain disturbed systems represented under initial and
measurement errors in linear discrete-time state-space. A novel bias-constrained a …

In this article, we develop a robust H 2 H _2 optimal unbiased FIR (H 2 H _2‐OUFIR)
predictor and receding horizon (RH) H 2 H _2‐OUFIR filter for uncertain and disturbed …

This paper deals with the issue of the robust H∞ FIR (Fmite Impulse Response) filtering
problem for linear systems with parameter uncertainty in the state-space model. Firstly, the …

In this paper, we develop a robust 2 optimal unbiased FIR (2-OUFIR) predictor and receding
horizon (RH) 2-OUFIR filter for uncertain and disturbed systems in the presence of …

In this correspondence, a robust filter is proposed to effectively estimate the system states in
the case where system model uncertainties as well as disturbances are present. The …

The H 2 finite impulse response (FIR) filtering approach allows for optimal object tracking
under harsh industrial conditions. In this brief, we propose a bias-constrained H 2 optimal …