  This paper addresses three complex control challenges related to
input-saturated systems from a data-driven perspective. Unlike the traditional
two-stage process involving system identification and model-based control, the
proposed approach eliminates the need for an explicit model description. The
method combines data-based closed-loop representations, Lyapunov theory,
instrumental variables, and a generalized sector condition to formulate
data-driven linear matrix inequalities (LMIs). These LMIs are applied to
maximize the origin's basin of attraction, minimize the closed-loop reachable
set with bounded disturbances, and introduce a new data-driven $\ell_2$-gain
minimization problem. Demonstrations on benchmark examples highlight the
advantages and limitations of the proposed approach compared to an explicit
identification of the system, emphasizing notable benefits in handling
nonlinear dynamics.
