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A comprehensive approach addressing identification and control forlearningbased Model Predictive Control (MPC) for linear systems is presented.The design technique yields a data-driven MPC law, based on a dataset collectedfrom the working plant. The method is indirect, i.e. it relies on a modellearning phase and a model-based control design one, devised in an integratedmanner. In the model learning phase, a twofold outcome is achieved: first,different optimal p-steps ahead prediction models are obtained, to be used inthe MPC cost function; secondly, a perturbed state-space model is derived, tobe used for robust constraint satisfaction. Resorting to Set Membershiptechniques, a characterization of the bounded model uncertainties is obtained,which is a key feature for a successful application of the robust controlalgorithm. In the control design phase, a robust MPC law is proposed, able totrack piece-wise constant reference signals, with guaranteed recursivefeasibility and convergence properties. The controller embeds multisteppredictors in the cost function, it ensures robust constraints satisfactionthanks to the learnt uncertainty model, and it can deal with possiblyunfeasible reference values. The proposed approach is finally tested in anumerical example.

Learning-based predictive control for linear systems: a unitary approach