In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology.

中文翻译：

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凸优化方法线性系统的输出反馈随机模型预测控制

本文针对离散时间线性时不变系统设计了随机模型预测控制器，考虑了加性扰动和随机约束。众所周知，在实际应用中，测量系统的所有状态信息通常是不可能或负担得起的。因此，在这项工作中，假设输出反馈律作为控制律。利用Chebyshev不等式和Schur补集，尝试将随机非凸优化问题转化为确定性凸优化问题。仿真结果证明了所提出方法的有效性。