A Stable Recursive Filter for State Estimation of Linear Models in the Presence of Bounded Disturbances
| Authors: | Becis-Aubry Yasmina, Université Henri Poincaré, Nancy 1; CRAN UMR 7039 CNRS, France, Metropolitan
Boutayeb Mohamed, Université Louis Pasteur, Strasbourg 1; LSIIT-CNRS., France, Metropolitan
Darouach Mohamed, Université Henri Poincaré, Nancy 1; CRAN UMR 7039 CNRS, France, Metropolitan |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Recursive Estimation Methods |
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| Keywords: | State estimation, Recursive algorithm, Stability. |
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Abstract
This contribution proposes a robust recursive algorithm for state estimation of linear multi-output systems with unknown but bounded disturbances corrupting both the state and measurement vectors.A novel approach based on state bounding techniques is presented.The proposed algorithm can be decomposed into two steps : time updating and observation updating that uses a switching estimation Kalman-like gain matrix. Particular emphasis will be given to the design of a weighting factor that ensures consistency of the estimated state vectors with the input-output data and the noise constraints and that enforces convergence properties.
