论文标题
递归可行的数据驱动的分布在稳健的模型模型控制和加性干扰
Recursively feasible Data-driven Distributionally Robust Model Predictive Control with additive disturbances
论文作者
论文摘要
在本文中，我们建议针对具有无界加性干扰的受约束随机系统进行数据驱动的分布强大的模型预测控制框架。通过对线性插值的初始状态约束与简化的仿射干扰反馈政策进行优化，可以确保递归可行性。我们考虑了在干扰的第二刻中基于矩的歧义设置，该歧义设置具有数据驱动的半径，在该时刻，我们得出了最少数量的样本，以确保在机会约束和闭环性能上的用户赋予置信度界限。该论文以数值示例结束，突出了基于不同样本量的性能增益和机会约束满意度。
In this paper we propose a data-driven distributionally robust Model Predictive Control framework for constrained stochastic systems with unbounded additive disturbances. Recursive feasibility is ensured by optimizing over an linearly interpolated initial state constraint in combination with a simplified affine disturbance feedback policy. We consider a moment-based ambiguity set with data-driven radius for the second moment of the disturbance, where we derive a minimum number of samples in order to ensure user-given confidence bounds on the chance constraints and closed-loop performance. The paper closes with a numerical example, highlighting the performance gain and chance constraint satisfaction based on different sample sizes.
