We analyze the stability properties of an approximate algorithm for moving horizon estimation (MHE). The strategy provides instantaneous state estimates and is thus suitable for large-scale feedback control. In particular, we study the interplay between numerical approximation errors and the convergence of the estimator error. In addition, we establish connections between the numerical properties of the Hessian of the MHE problem and traditional observability definitions. We demonstrate the developments through a simulation case study
In this research, real-time Moving Horizon Estimation (MHE) algorithms are developed for on-line sta...
A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable Tennessee...
Moving horizon estimation (MHE) is a con- strained non-convex optimization problem in principle, whi...
Abstract — In the last decade, moving horizon estimation (MHE) has emerged as a powerful technique f...
Abstract — In the past decade, moving horizon estimation (MHE) has emerged as a powerful technique f...
This note proposes a new form of nonlinear state estimator, for which we can establish robust global...
International audienceThis paper proposes a discussion on the classification of the formulations of ...
A novel type of iterative partition-based moving horizon estimators (PMHE) is proposed, the estimate...
Long horizon lengths in Moving Horizon Estimation are desirable to reach the performance limits of t...
In this paper, a constrained moving horizon estimation (MHE) strategy for linear systems is proposed...
Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state estima-tion. D...
Abstract: Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state esti...
This paper formalises the concepts of weakly and weakly regularly persistent input trajectory as wel...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon ...
By now many results with respect to the fast and efficient implementation of model predictive contro...
In this research, real-time Moving Horizon Estimation (MHE) algorithms are developed for on-line sta...
A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable Tennessee...
Moving horizon estimation (MHE) is a con- strained non-convex optimization problem in principle, whi...
Abstract — In the last decade, moving horizon estimation (MHE) has emerged as a powerful technique f...
Abstract — In the past decade, moving horizon estimation (MHE) has emerged as a powerful technique f...
This note proposes a new form of nonlinear state estimator, for which we can establish robust global...
International audienceThis paper proposes a discussion on the classification of the formulations of ...
A novel type of iterative partition-based moving horizon estimators (PMHE) is proposed, the estimate...
Long horizon lengths in Moving Horizon Estimation are desirable to reach the performance limits of t...
In this paper, a constrained moving horizon estimation (MHE) strategy for linear systems is proposed...
Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state estima-tion. D...
Abstract: Moving Horizon Estimation (MHE) is an efficient optimization-based strategy for state esti...
This paper formalises the concepts of weakly and weakly regularly persistent input trajectory as wel...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon ...
By now many results with respect to the fast and efficient implementation of model predictive contro...
In this research, real-time Moving Horizon Estimation (MHE) algorithms are developed for on-line sta...
A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable Tennessee...
Moving horizon estimation (MHE) is a con- strained non-convex optimization problem in principle, whi...
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