Unfalsified approach to data-driven control design

The paper deals with the problem of designing controllers from experimental data. We propose a non-iterative direct approach in which the parameters of a controller of a prescribed order and structure are optimized with respect to a relevant performance criterion. The proposed approach builds upon the so-called unfalsified control theory. This is the key point which makes it possible to derive simple and intuitive relations between the choice of the performance criterion to optimize and closed-loop stability conditions, thus making it possible to derive a data-driven controller tuning procedure incorporating simple stability tests. An example is presented to substantiate the analysis.