This paper considers constrained linear systems with stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. We propose a model predictive control (MPC) law that minimises a discounted cost subject to a discounted expectation constraint. Sensor data is assumed to be lost with known probability, and data losses are accounted for by expressing the predicted control policy as an affine function of future observations, which results in a convex optimal control problem. An online constraint-tightening technique ensures recursive feasibility of the online optimisation problem and satisfaction of the expectation constraint without imposing bounds on the distributions of the noise and disturbance inputs. The discounted cost evaluated along trajectories of the closed loop system is shown to be bounded by the initial optimal predicted cost. We also provide conditions under which the averaged undiscounted closed loop cost accumulated over an infinite horizon is bounded. Numerical simulations are described to illustrate these results.

中文翻译：

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具有间歇性观察的随机输出反馈 MPC

本文考虑了通过有损通信信道传输的具有随机加性扰动和噪声测量的约束线性系统。我们提出了一种模型预测控制 (MPC) 定律，该定律可在贴现期望约束下最小化贴现成本。假设传感器数据以已知概率丢失，数据丢失通过将预测控制策略表示为未来观测的仿射函数来解释，这导致凸最优控制问题。在线约束紧缩技术确保了在线优化问题的递归可行性和期望约束的满足，而不会对噪声和干扰输入的分布施加界限。沿着闭环系统的轨迹评估的折扣成本被证明受初始最优预测成本的限制。我们还提供了在无限范围内累积的平均未贴现闭环成本有界的条件。描述了数值模拟来说明这些结果。