This PhD thesis is dedicated to the stability analysis of sampled-data systems with time-varying sampling, and to the dynamic control of the sampling instants. The main objective is to design sampling laws that allow for reducing the sampling frequency of state-feedback control for linear systems while ensuring the system's stability.First, an overview of the recent problems, challenges, and research directions regarding sampled-data systems is presented. Then, a novel dynamic sampling control approach, "state-dependent sampling", is proposed. It allows for designing offline a maximal state-dependent sampling map over conic regions of the state space, thanks to LMIs.Various classes of systems are considered throughout the thesis. First, we consider the case of ideal LTI systems, and propose a sampling map design based on the use of polytopic embeddings and Lyapunov-Razumikhin exponential stability conditions. Then, the robustness with respect to exogenous perturbations is included. Different applications are proposed: robust stability analysis with respect to time-varying sampling, as well as event-triggered, self-triggered, and state-dependent sampling control schemes. Finally, a sampling map design is proposed in the case of perturbed LTI systems with delay in the feedback control loop. It is based on L2-stability conditions and a novel type of Lyapunov-Krasovskii functionals with state-dependent matrices. Here, the stabilization issue is considered, and a new controller with gains that switch according to the system's state is presented. A co-design controller gains/sampling map is then proposed
