  We present a novel data-driven Model Predictive Control (MPC) algorithm for
nonlinear systems. The method is based on recent extensions of behavioural
theory and Willem's Fundamental Lemma for nonlinear systems by the means of
adequate Input-Output (IO) quasi-Linear Parameter Varying (qLPV) embeddings.
Thus, the MPC is formulated to ensure regulation and IO constraints
satisfaction, based only on measured datasets of sufficient length (and under
persistent excitation). Instead of requiring the availability of the scheduling
trajectories (as in recent papers), we consider an estimate of the function
that maps the qLPV realisation. Specifically, we use an extrapolation procedure
in order to generate the future scheduling trajectories, at each sample, which
is shown to be convergent and generated bounded errors. Accordingly, we discuss
the issues of closed-loop IO stability and recursive feasibility certificates
of the method. The algorithm is tested and discussed with the aid of a
numerical application.
