The Direct Data-Driven Control (DDDC) is an approach to controller design which solely relies on data collected though an experiment on the plant. This approach is mainly advantageous when it is difficult to obtain an accurate model for the system, or when the dynamics of the plant is known to be nonlinear and, in general, very complex. The DDDC approach is very flexible, because it can adapt to changes and can be more robust to uncertainties rather than classical control techniques. This thesis explores the main techniques developed within the framework of DDDC, pointing out both their strengths and limitations. Based on the model-matching formulation, we present an alternative approach that aims to ensure that the controller designed with our method results to be stable and robust to uncertainties, even in the presence of noise in the data.