We discuss a new method for the data-based design of feedback controllers in a linear setting. The main features of the method are that it is a direct method (no model identification of the plant is needed) and that it can be applied using a single set of data generated by the plant, needing no specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the significant case of restricted complexity controller design, the achieved controller is a sensible approximation (under some reasonable hypotheses) of the restricted complexity global optimal controller. As an extra contribution it is also presented a controller validation test aiming at ascertain the closed-loop stability before the designed controller is applied to the plant. A numerical example is given.
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