发表机构
Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出统一框架，利用离线数据设计鲁棒终端要素并融入一步MPC，保证递归可行性与实际指数稳定性，减少预测时域和在线计算量。
AI 中文摘要
本文针对未知线性时不变系统，提出了一种具有终端要素的鲁棒数据驱动模型预测控制（MPC）的统一框架。基于Willems基本引理，使用单条离线输入输出轨迹作为隐式模型，而在线实现仅需过去的输入输出测量值。在离线与在线的输出数据中均考虑了有界测量噪声。该框架包括离线阶段和在线阶段：离线阶段直接从含噪声数据设计鲁棒终端要素，在线阶段将这些要素纳入一步MPC方案。本文建立了所提框架的递归可行性和闭环系统的实际指数稳定性。数值结果展示了终端集合的性能调节能力，尤其在短预测时域下效果显著，并表明所提框架在减少预测时域和在线计算量的同时，达到了与无终端要素的数据驱动MPC相当的性能。
英文摘要
This paper proposes a unified framework for robust data-driven model predictive control (MPC) with terminal ingredients for unknown linear time-invariant systems. Based on Willems' fundamental lemma, a single offline input-output trajectory is used as an implicit model, while online implementation only requires past input-output measurements. Bounded measurement noise is considered in both offline and online output data. The framework consists of an offline stage, where robust terminal ingredients are designed directly from noisy data, and an online stage, where these ingredients are incorporated into a one-step MPC scheme. Recursive feasibility of the proposed framework and practical exponential stability of the closed-loop system are established. Numerical results illustrate the performance tuning capability of the terminal set, with particularly pronounced benefits for short prediction horizons, and show that the proposed framework achieves performance comparable to data-driven MPC without terminal ingredients while requiring a reduced prediction horizon and less online computation.
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