  The paper describes a receding horizon control design framework for
continuous-time stochastic nonlinear systems subject to probabilistic state
constraints. The intention is to derive solutions that are implementable in
real-time on currently available mobile processors. The approach consists of
decomposing the problem into designing receding horizon reference paths based
on the drift component of the system dynamics, and then implementing a
stochastic optimal controller to allow the system to stay close and follow the
reference path. In some cases, the stochastic optimal controller can be
obtained in closed form; in more general cases, pre-computed numerical
solutions can be implemented in real-time without the need for on-line
computation. The convergence of the closed loop system is established assuming
no constraints on control inputs, and simulation results are provided to
corroborate the theoretical predictions.
