Set-theoretic receding horizon control for nonlinear systems: a data-driven approach

In this paper, we propose a data-driven model predictive control scheme for constrained single-input single-output nonlinear systems. The idea is to customize a set-theoretic receding horizon controller within a data driven setting. To this end, the key ingredients of the considered strategy are achieved by properly converting model-based conditions in terms of data series on the available input and output signals.