Mean-Square Stability of Continuous-Time Stochastic Model Predictive Control

Abstract

We propose a stochastic model predictive control (SMPC) framework for a broad class of unconstrained controlled stochastic differential equations (SDEs) and establish its mean-square exponential stability in the infinite-horizon limit. At each prediction step of the MPC iteration, the nonlinear controlled SDE is approximated by its linearization at the origin, with the sampled state of the nonlinear system as initial condition, yielding a finite-horizon stochastic linear-quadratic (SLQ) optimal control problem. The resulting optimal control is then applied to the original nonlinear stochastic dynamics until the next sampling instant. This construction leads to a delayed SMPC scheme whose closed-loop behavior is governed by a coupled time-delay SDE system, a setting that has not been analyzed before. We prove global mean-square exponential stability for linear and mildly nonlinear SDEs by exploiting the exponential convergence of the Riccati equation to the algebraic Riccati equation (ARE). For strongly nonlinear SDEs, we establish local mean-square exponential stability by combining exponential Riccati convergence with stopping-time techniques and Grönwall-type estimates. It is observed that, to ensure the desired local stability properties, the nonlinearities of the SDE are allowed to have polynomial growth but not exponential growth, distinguishing SMPC from its deterministic counterpart. These results provide the first rigorous mean-square stability guarantees for SMPC of SDE systems with delayed state information, thereby advancing the theoretical foundations of stochastic predictive control.