Model predictive force control in milling based on an ensemble Kalman filter

Unscented Kalman Filter in Adaptive Neural Model-based Predictive Control

An adaptive model-based predictive control scheme is proposed for non-linear systems. This methodology exploits the non-linear modelling capabilities of nonlinear state-space neural networks and the online weights adjustment by means of an unscented Kalman filter. Results from experiments show evidences on its good tracking performance even when the system’s dynamics change.

Controlling balance in an ensemble Kalman filter

We present a method to control unbalanced fast dynamics in an ensemble Kalman filter by introducing a weak constraint on the imbalance in a spatially sparse observational network. We show that the balance constraint produces significantly more balanced analyses than ensemble Kalman filters without balance constraints and than filters implementing incremental analysis updates (IAU). Furthermore,...

Impacts of Model Error and Ensemble Initiation on Mesoscale Data Assimilation with an Ensemble Kalman Filter

An Image-based Ensemble Kalman Filter for Motion Estimation

This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only from image properties, to estimate motion on image sequences. The key elements of this filter are, first, the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied temporal interval. Both are analyzed in the paper and their impact on results is ...

Tutorial on the Ensemble Kalman Filter ∗

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component o...