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Conference Papers Year : 2021

Interval Estimation for Discrete-Time Linear Parameter-Varying System with Unknown Inputs

Abstract

This paper proposes a new interval observer for joint estimation of the state and unknown inputs of a discrete-time linear parameter-varying (LPV) system with an unmeasurable parameter vector. This system is assumed to be subject to unknown inputs and unknown but bounded disturbances and measurement noise, while the parameter-varying matrices are elementwise bounded. Considering the unknown inputs as auxiliary states, the dynamics are rewritten as discrete-time LPV descriptor dynamics. A new structure of interval observer is then used, providing more degrees of freedom than the classical change of coordinates-based structure. The observer gains are computed by solving linear matrix inequalities derived from cooperativity condition and L∞ norm. Numerical simulations are run to show the efficiency of the proposed observer.
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Dates and versions

hal-03332064 , version 1 (15-02-2022)

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Thomas Chevet, Thach Ngoc Dinh, Julien Marzat, Tarek Raissi. Interval Estimation for Discrete-Time Linear Parameter-Varying System with Unknown Inputs. 60th IEEE Conference on Decision and Control, Dec 2021, Austin, TX, United States. pp.4002-4007, ⟨10.1109/CDC45484.2021.9683335⟩. ⟨hal-03332064⟩
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