Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance constraints, which enables a systematic trade-off between attainable control performance and probability of state constraint violations in a stochastic setting. This paper presents an overview of core concepts in SMPC in relation to MPC and stochastic optimal control, with numerical illustrations on a typical chemical process. Estimation of stochastic disturbances as well as the impact of estimation quality of stochastic disturbances on the SMPC performance are discussed. Some avenues for future research in SMPC are suggested.

随机模型预测控制（SMPC）为具有随机不确定性的系统的MPC提供了一个概率框架。SMPC的一个关键特征是包含了机会约束，这使得可以在随机环境中在可获得的控制性能和违反状态约束的可能性之间进行系统的权衡。本文概述了SMPC中与MPC和随机最优控制相关的核心概念，并给出了典型化学过程的数值说明。讨论了随机干扰的估计以及随机干扰的估计质量对SMPC性能的影响。提出了一些将来在SMPC中进行研究的途径。