  Model predictive control strategies require to solve in an sequential manner,
many, possibly non-convex, optimization problems. In this work, we propose an
interacting stochastic agent system to solve those problems. The agents evolve
in pseudo-time and in parallel to the time-discrete state evolution. The method
is suitable for non-convex, non-differentiable objective functions. The
convergence properties are investigated through mean-field approximation of the
time-discrete system, showing convergence in the case of additive linear
control. We validate the proposed strategy by applying it to the control of a
stirred-tank reactor non-linear system.
