An Extended Kalman Filter for Data-enabled Predictive Control
Optimization and Control
2020-05-12 v2
Abstract
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant systems in which the uncertainty (if any) is assumed to be deterministic and bounded; relatively little attention has been devoted to stochastic linear time-invariant systems. As a first step in this direction, we propose to equip the recently introduced Data-enabled Predictive Control algorithm with a data-based Extended Kalman Filter to make use of additional available input-output data for reducing the effect of noise, without increasing the computational load of the optimization procedure.
Cite
@article{arxiv.2003.08269,
title = {An Extended Kalman Filter for Data-enabled Predictive Control},
author = {Daniele Alpago and Florian Dorfler and John Lygeros},
journal= {arXiv preprint arXiv:2003.08269},
year = {2020}
}