论文标题
在数据驱动的随机输出反馈预测控制上
On Data-Driven Stochastic Output-Feedback Predictive Control
论文作者
论文摘要
Jan C. Willems和合着者的基本引理可以通过测量的输入输出数据来表示线性时间不变系统的所有输入输出轨迹。事实证明，该结果对于数据驱动控制是关键的。本文以基本引理的随机变体为基础，为随机线性时间流（LTI）系统提供了数据驱动的输出反馈预测控制方案。所考虑的LTI系统受到非高斯干扰的约束，仅知道有关其前两个时刻的信息。利用多项式混乱的扩展，提出的方案以数据驱动的随机最佳控制问题（OCP）为中心。通过对初始条件的量身定制的在线设计，我们为基于OCP终端成分的数据驱动设计设计提供了足够的条件，以实现提出的输出反馈方案的递归可行性。此外，我们提供了闭环性能的鲁棒性分析。数值示例说明了提出的方案的功效。
The fundamental lemma by Jan C. Willems and co-authors enables the representation of all input-output trajectories of a linear time-invariant system by measured input-output data. This result has proven to be pivotal for data-driven control. Building on a stochastic variant of the fundamental lemma, this paper presents a data-driven output-feedback predictive control scheme for stochastic Linear Time-Invariant (LTI) systems. The considered LTI systems are subject to non-Gaussian disturbances about which only information about their first two moments is known. Leveraging polynomial chaos expansions, the proposed scheme is centered around a data-driven stochastic Optimal Control Problem (OCP). Through tailored online design of initial conditions, we provide sufficient conditions for the recursive feasibility of the proposed output-feedback scheme based on a data-driven design of the terminal ingredients of the OCP. Furthermore, we provide a robustness analysis of the closed-loop performance. A numerical example illustrates the efficacy of the proposed scheme.
