  A receding horizon learning scheme is proposed to transfer the state of a
discrete-time dynamical control system to zero without the need of a system
model. Global state convergence to zero is proved for the class of stabilizable
and detectable linear time-invariant systems, assuming that only input and
output data is available and an upper bound of the state dimension is known.
The proposed scheme consists of a receding horizon control scheme and a
proximity-based estimation scheme to estimate and control the closed-loop
trajectory. Simulations are presented for linear and nonlinear systems.
