Code and data belonging to the publication "Closed-loop Aspects of Data-Enabled Predictive Control"

This code allows one to generate figures that correspond to the publication "Closed-loop Aspects of Data-Enabled Predictive Control".

Data that derives from this code is provided separately in this upload (see paper_probleemstelling_24n120.mat file). Use of this .mat file allows direct reproduction of the figures without running new simulations. See the README.md file for instructions.

Abstract:

In recent years, the amount of data available from systems has drastically increased, mo-

tivating the use of direct data-driven control techniques that avoid the need of parametric

modeling. The aim of this chapter is to analyze closed-loop aspects of these approaches

in the presence of noise. To analyze this, a unified formulation of several approaches,

including DeePC and SPC is obtained and the influence of noise on closed-loop predictors

is analyzed. The analysis reveals potential closed-loop correlation problems, which are

closely related to well-known results in closed-loop system identification, and consequent

control issues. A case study reveals the hazards of noise in data-driven control.