**3. Bypassing Models Using Data**

The main objective of control design based on the data-driven approach is enabling the possibility to synthesize controllers entirely from data of available measurements, which is a simplified route with respect to the classic system identification (modeling and parametric identification) plus a model-based control approach. This is illustrated in Figure 1.

**Figure 1.** Proposed data-driven control vs. traditional "system identification + model-based" approach.

Moreover, stability conditions and the desired performance must be completely comparable with a model-based scheme. This is also in sharp contrast with basic empiric gain-tuning rules for classic P, PI, and PID controllers, which do not require a model of the system, but cannot guarantee stability and performance in a deterministic way (see [22,23]). Our present conviction is to generate a controller purely from measurements that permits to omit the need for an explicit mathematical model without losing stability and general performance capabilities with respect to a model-based technique.

The process begins with the establishment of essential conditions to assure that the measured data are convenient for control design. In brief, we introduce a test that determines whether the information provided by the available data is sufficient to fully recuperate the system physical laws.
