We are proposing state estimators for nonlinear systems. Our techniques extend a previous work on state reconstructors for linear systems by the same authors (Reconstructeurs d'états, C.R. Acad. Sci. Paris, Série I, 338, 2004, 91-96), which bypasses some of the classic difficulties related to asymptotic observers and Kalman filtering (lack of robustness and knowledge of statistics). Our viewpoint, which avoids the integration of differential equations and therefore any asymptotic estimation, yields fast implementable algebraic formulae. Two concrete casestudies are presented, which are (differentially) flat. Our state estimation permits a state feedback control around the flatness-based reference trajectory. Convincing simulations are provided which demonstrate the robustness of our control strategy with respect to noises with unknown statistical properties.