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Zonotopic Kalman Filter-based Interval Estimation for Discrete-Time Linear Systems with Unknown Inputs

Abstract : This letter proposes an unknown input zonotopic Kalman filter-based interval observer for discrete-time linear time-invariant systems. In such contexts, a change of coordinates decoupling the state and the unknown inputs is often used. Here, the dynamics are rewritten into a discrete-time linear time-invariant descriptor system by augmenting the state vector with the unknown inputs. A zonotopic outer approximation of the feasible state set is then obtained with a prediction-correction strategy using the information from the system dynamics, known inputs and outputs. Bounds for both the state and unknown inputs are obtained from this zonotopic set. The efficiency of the proposed interval observer is assessed with numerical simulations.
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https://hal.archives-ouvertes.fr/hal-03332068
Contributor : Thomas Chevet Connect in order to contact the contributor
Submitted on : Thursday, September 2, 2021 - 2:34:29 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM

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Thomas Chevet, Thach Ngoc Dinh, Julien Marzat, Zhenhua Wang, Tarek Raissi. Zonotopic Kalman Filter-based Interval Estimation for Discrete-Time Linear Systems with Unknown Inputs. 60th IEEE Conference on Decision and Control, Dec 2021, Austin, TX, United States. ⟨10.1109/LCSYS.2021.3086562⟩. ⟨hal-03332068⟩

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