International audienceFor linear time-invariant dynamic systems with exactly known coefficients of their system matrices for which measurements with bounded errors are available at discrete time instants, an optimal polygonal state estimation scheme was recently published. This scheme allows for tightly enclosing all possible state trajectories in presence of uncertain, but bounded, system inputs which may be varying arbitrarily within in their bounds. Moreover, this approach is also capable of accounting for uncertainty related to the measurement time instants. However, the drawback of this polygonal technique is its rapidly increasing complexity for larger system dimensions. For that reason, the polygonal state enclosures are replaced by ...
In this article, we consider the problem of discrete-time linear state estimation when at every disc...
This paper presents a modified set theoretic framework for estimating the state of a linear dynamic ...
In this paper, a new filter model called set-membership Kalman filter for nonlinear state estimation...
International audienceFor linear time-invariant dynamic systems with exactly known coefficients of t...
This paper proposes a new ellipsoid-based guaranteed state estimation approach for linear discrete-t...
Groupe de Travail de Commande Prédictive Non Linéaire / Méthodes Ensemblistes pour l’Automatique, Pa...
International audienceSimulating dynamic systems with bounded uncertainty in initial conditions and ...
International audienceThis paper proposes an interval-based method for estimating the state of a lin...
This paper presents a setmembership state estimation scheme for linear systems with unknown but boun...
International audienceThis paper presents a new online ellipsoidal guaranteed set-membership state e...
A numerically stable recursive set membership state estimation algorithm for linear discrete-time sy...
The 19th World Congress of the International Federation of Automatic Control 2014. Cape Town, Sudáfr...
International audienceThe verified simulation of initial value problems (IVPs) for ordinary differen...
In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state es...
This paper gives a concise description of effective solutions to the guaranteed state estimation pro...
In this article, we consider the problem of discrete-time linear state estimation when at every disc...
This paper presents a modified set theoretic framework for estimating the state of a linear dynamic ...
In this paper, a new filter model called set-membership Kalman filter for nonlinear state estimation...
International audienceFor linear time-invariant dynamic systems with exactly known coefficients of t...
This paper proposes a new ellipsoid-based guaranteed state estimation approach for linear discrete-t...
Groupe de Travail de Commande Prédictive Non Linéaire / Méthodes Ensemblistes pour l’Automatique, Pa...
International audienceSimulating dynamic systems with bounded uncertainty in initial conditions and ...
International audienceThis paper proposes an interval-based method for estimating the state of a lin...
This paper presents a setmembership state estimation scheme for linear systems with unknown but boun...
International audienceThis paper presents a new online ellipsoidal guaranteed set-membership state e...
A numerically stable recursive set membership state estimation algorithm for linear discrete-time sy...
The 19th World Congress of the International Federation of Automatic Control 2014. Cape Town, Sudáfr...
International audienceThe verified simulation of initial value problems (IVPs) for ordinary differen...
In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state es...
This paper gives a concise description of effective solutions to the guaranteed state estimation pro...
In this article, we consider the problem of discrete-time linear state estimation when at every disc...
This paper presents a modified set theoretic framework for estimating the state of a linear dynamic ...
In this paper, a new filter model called set-membership Kalman filter for nonlinear state estimation...