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This paper presents a direct data-driven approach for computing robustcontrol invariant (RCI) sets and their associated state-feedback control lawsfor linear time-invariant systems affected by bounded disturbances. Theproposed method utilizes a single state-input trajectory generated from thesystem, to compute a polytopic RCI set with a desired complexity and aninvariance-inducing feedback controller, without the need to identify a modelof the system. The problem is formulated in terms of a set of sufficient linearmatrix inequality conditions that are then combined in a semi-definite programto maximize the volume of the RCI set while respecting the state and inputconstraints. We demonstrate through a numerical case study that the proposeddata-driven approach can generate RCI sets that are of comparable size to thoseobtained by a model-based method in which exact knowledge of the systemmatrices is assumed.

Direct Data-Driven Computation of Polytopic Robust Control Invariant Sets and State-Feedback Controllers