  In this paper, a simple heuristic is proposed for the design of uncertainty
aware predictive controllers for nonlinear models involving uncertain
parameters. The method relies on Machine Learning-based approximation of ideal
deterministic MPC solutions with perfectly known parameters. An efficient
construction of the learning data set from these off-line solutions is proposed
in which each solution provides many samples in the learning data. This enables
a drastic reduction of the required number of Non Linear Programming problems
to be solved off-line while explicitly exploiting the statistics of the
parameters dispersion. The learning data is then used to design a fast on-line
output dynamic feedback that explicitly incorporate information of the
statistics of the parameters dispersion. An example is provided to illustrate
the efficiency and the relevance of the proposed framework. It is in particular
shown that the proposed solution recovers up to 78\% of the expected advantage
of having a perfect knowledge of the parameters compared to nominal design.
