This work addresses data-based (DB) control design; the properties and limitations inherent to DB design are discussed under a common theoretical framework and illustrated through experimental results. Theoretical results concerning the convergence and precision are discussed and specified for a particular class of processes. Two DB methods, representative of this design approach, are used to illustrate the general properties of DB design: the Virtual Reference Feedback Tuning (VRFT) and the Iterative Feedback Tuning (IFT).

This study addresses the control of linear systems subject to both sensor and actuator saturations and additive L2-bounded disturbances. Supposing that only the output of the linear plant is measurable, the synthesis of stabilising output feedback dynamic controllers, allowing to ensure the internal closed-loop stability and the finite L2-gain stabilisation, is considered. In this case, it is shown that the closed-loop system presents a nested saturation term. Therefore, based on the use of some modified sector conditions and appropriate variable changes, synthesis conditions in a quasi-linear matrix inequality (LMI) form are stated in both regional (local) as well as global stability contexts. Different LMI-based optimisation problems for computing a controller in order to maximise the disturbance tolerance, the disturbance rejection or the region of stability of the closed-loop system are proposed.