Data-driven Correlation Approach Applied to Load Disturbance Rejection in a Thermal Process

From a practical point of view, adjusting the controller without having to identify the process model has many advantages, for example, when the process is simple but changes a lot during the operation. In this case, there are many direct data-driven methods in the literature which may be employed to adjust a monovariable controller aiming at reference tracking. However, when the control objective is disturbance rejection or regulation, the designer is left with too few choices. The aim of this paper is to provide one new option and show how it can be applied to those control objectives.