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We address the problem of constructing asymptotic observers for linear time-invariant systems with measurements subject to outliers. We propose a linear observer with a saturated output injection, which allows one to roughly mitigate the effect of abnormal and isolated measurement errors on the error dynamics. In addition to proposing the observer architecture, we provide convex conditions on the...
The stability of filters for nonlinear dynamic systems with Lipschitz nonlinearities is addressed. By considering the disturbances in the system and measurement equations as the inputs to the system representing the estimation error dynamics, the input-output stability of the filtering mapping associated with the estimation error dynamics is introduced. This is done with respect to signals belonging...
In the last decade, moving horizon estimation (MHE) has emerged as a powerful technique for tackling the problem of estimating the state of a dynamic system in the presence of nonlinearities and disturbances. MHE is based on the idea of minimizing an estimation cost function defined on a sliding window composed of a finite number of time stages. The cost function is usually made up of two contributions:...
A full-order sliding-mode state observer for a class of nonlinear continuous-time dynamic systems is proposed and conditions for the stability of the estimation error in the absence of noises are provided. If the system is affected by bounded disturbances, under such conditions the existence of an attractive invariant set for the estimation error is ensured. The design of the observer can be made...
A full-order sliding-mode state observer for a class of nonlinear continuous-time dynamic systems is proposed and conditions for the stability of the estimation error in the absence of noises are provided. If the system is affected by bounded disturbances, under such conditions the existence of an attractive invariant set for the estimation error is ensured. The design of the observer can be made...
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