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The paper describes a receding horizon control design framework forcontinuous-time stochastic nonlinear systems subject to probabilistic stateconstraints. The intention is to derive solutions that are implementable inreal-time on currently available mobile processors. The approach consists ofdecomposing the problem into designing receding horizon reference paths basedon the drift component of the system dynamics, and then implementing astochastic optimal controller to allow the system to stay close and follow thereference path. In some cases, the stochastic optimal controller can beobtained in closed form; in more general cases, pre-computed numericalsolutions can be implemented in real-time without the need for on-linecomputation. The convergence of the closed loop system is established assumingno constraints on control inputs, and simulation results are provided tocorroborate the theoretical predictions.

Stochastic receding horizon control of nonlinear stochastic systems with probabilistic state constraints