  In this paper, a supervised clustering based-heuristic is proposed for the
real-time implementation of approximate solutions to stochastic nonlinear model
predictive control frameworks. The key idea is to update on-line a low
cardinality set of uncertainty vectors to be used in the expression of the
stochastic cost and constraints. These vectors are the centers of uncertainty
clusters that are built using the optimal control sequences, cost and
constraints indicators as supervision labels. The use of a moving clustering
data buffer which accumulates recent past computations enables to reduce the
computational burden per sampling period while making available at each period
a relevant amount of samples for the clustering task. A relevant example is
given to illustrate the contribution and the associated algorithms.
