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# Control of Unknown (Linear) Systems with Receding Horizon Learning

*Proceedings of the 3rd Conference on Learning for Dynamics and Control*, PMLR 144:584-596, 2021.

#### Abstract

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