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- W4315488957 abstract "In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems. The method consists of augmenting the system dynamics with an approximated ultra-local model (a finite chain of integrators) for the fault vector and constructing a Nonlinear Unknown Input Observer (NUIO) for the augmented dynamics. Then, fault reconstruction is reformulated as a robust state estimation problem in the augmented state (true state plus fault-related state). We provide sufficient conditions that guarantee the existence of the observer and stability of the estimation error dynamics (asymptotic stability of the origin in the absence of faults and ISS guarantees in the faulty case). Then, we cast the synthesis of observer gains as a semidefinite program where we minimize the ℒ <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</inf> -gain from the model mismatch induced by the approximated fault model to the fault estimation error. Finally, simulations are given to illustrate the performance of the proposed methodology." @default.
- W4315488957 created "2023-01-11" @default.
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- W4315488957 date "2022-12-06" @default.
- W4315488957 modified "2023-09-27" @default.
- W4315488957 title "Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction" @default.
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- W4315488957 doi "https://doi.org/10.1109/cdc51059.2022.9992945" @default.
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