  This paper presents a method to identify an uncertain linear time-invariant
(LTI) prediction model for tube-based Robust Model Predictive Control (RMPC).
The uncertain model is determined from a given state-input dataset by
formulating and solving a Semidefinite Programming problem (SDP), that also
determines a static linear feedback gain and corresponding invariant sets
satisfying the inclusions required to guarantee recursive feasibility and
stability of the RMPC scheme, while minimizing an identification criterion. As
demonstrated through an example, the proposed concurrent approach provides less
conservative invariant sets than a sequential approach.
