  Nonlinear model predictive control (NMPC) has gained widespread use in many
applications. Its formulation traditionally involves repetitively solving a
nonlinear constrained optimization problem online. In this paper, we
investigate NMPC through the lens of Bayesian estimation and highlight that the
Monte Carlo sampling method can offer a favorable way to implement NMPC. We
develop a constraint-aware particle filtering/smoothing method and exploit it
to implement NMPC. The new sampling-based NMPC algorithm can be executed easily
and efficiently even for complex nonlinear systems, while potentially
mitigating the issues of computational complexity and local minima faced by
numerical optimization in conventional studies. The effectiveness of the
proposed algorithm is evaluated through a simulation study.
