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In all cases, proper tuning of the design parameters, i.e., the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the estimation error to zero.
Note that all these filter gains are given in the batch form, where the computational complexity is large when the estimation horizon is long.
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This region is then used to design a constraint on the state estimates in the proposed moving horizon estimation.
First, the proposed moving horizon estimation scheme is proved to give bounded estimation errors in the case of bounded model uncertainties.
A moving horizon estimation (MHE) approach to simultaneously estimate states and parameters is revisited.
In this study, a nonlinear battery state-space model based moving horizon estimation (MHE) approach is proposed to estimate SOC within the full range.
The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme.
Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates.
Moving Horizon Estimation (MHE) has been a popular approach in chemical engineering community for the estimation of both ODE and differential algebraic equation (DAE) systems but is computationally demanding.
The estimator proposed within the framework of this work is similar to the Moving Horizon Estimation strategy.
In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com