Selection of informative intervals in routine operating data for use in data-driven control design

Estimating the controller's parameters using a data-driven method usually requires to collect data from a specific experiment, which can be a very costly and undesirable task. A possibility to relief this burden is to use routine operating data. However, the blind application of such data may lead to inappropriate tuning and undesirable closed-loop behaviour due to lack of informativity in the data. Therefore, it is necessary to identify the informative subsets within the entire data set. The present paper is an extension of our previous work where two data selection criteria, developed to the system identification framework, were applied to the data-driven control design problem focusing on the case of full-order controllers. In this paper we treat the case of reduced order controllers, and show that only one of those two criteria can be applied to this case. The feasibility and robustness of the proposed solution is presented through Monte-Carlo simulations in a case study.