In this paper, we develop two algorithms for stochastic model predictive control (SMPC) problems with discrete linear systems. Participially, chance constraints on the state and control are considered. Different from the state-of-the-art robust model predictive control (RMPC) algorithm, the proposed is less conservative. Meanwhile, the proposed algorithms do not assume the full knowledge of the disturbance distribution. It only requires the mean and variance of the disturbance. Rigorous computational analysis is carried out for the proposed algorithms. Numerical results are provided to demonstrate the effectiveness and the superior of the proposed SMPC algorithms.

中文翻译：

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具有扰动反馈的机会约束随机模型预测控制问题

在本文中，我们针对离散线性系统的随机模型预测控制（SMPC）问题开发了两种算法。特别地，考虑了对状态和控制的机会约束。与最新的鲁棒模型预测控制（RMPC）算法不同，该提议的保守性较低。同时，所提出的算法没有充分了解扰动分布。它只需要扰动的均值和方差。对提出的算法进行了严格的计算分析。数值结果证明了所提SMPC算法的有效性和优越性。