发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种仅基于输入-输出数据的随机数据驱动预测控制框架，利用扰动数据估计和因果子空间预测器，通过收紧约束保证机会约束满足，并具备递归可行性与闭环保证。
AI 中文摘要
我们提出了一种随机数据驱动预测控制（DPC）框架，用于受次高斯加性扰动的离散时间线性时不变系统，该框架仅基于输入-输出数据。与依赖精确扰动数据或至少需要样本生成以实现闭环保证的相关方法不同，所提出的方法利用扰动数据估计。通过强制扰动数据估计与可用输入-输出数据和系统类别的一致性，我们首先识别出数据驱动且可证明因果的子空间预测器，用于DPC。然后，我们分析相应预测误差的统计特性，从而为名义预测生成收紧的约束，以保证机会约束的满足。所提出的DPC方案在标准假设下具有递归可行性和闭环中条件机会约束满足的保证。数值评估研究证明了所提出控制器的性能。
英文摘要
We present a stochastic data-driven predictive control (DPC) framework for discrete-time linear time-invariant systems subject to sub-Gaussian additive disturbances based solely on input--output data. In contrast to related methods that rely on exact disturbance data or at least sample generation for closed-loop guarantees, the proposed approach leverages a disturbance data estimate. By enforcing consistency of the disturbance data estimate with the available input--output data and system class, we first identify data-driven and provably causal subspace predictors for use in DPC. Then, we analyze statistical properties of the corresponding prediction error, yielding tightened constraints for the nominal predictions that guarantee satisfaction of chance constraints. The proposed DPC scheme comes with guarantees on recursive feasibility and conditional chance constraint satisfaction in closed-loop under standard assumptions. A numerical evaluation study demonstrates the performance of the proposed controller.
CommentsThis manuscript is a revised version of the article published in IFAC Journal of Systems and Control (2026), vol. 25, pp. 100399. It contains corrections of typos relative to the published version
Journal refIFAC Journal of Systems and Control (2026), vol. 25, pp. 100399
DOI：10.1016/j.ifacsc.2026.100399
