Online Data-Driven MPC for Unknown Switched Linear Systems: A Hybrid Sampling Approach

• 成果类型: Article
• 署名作者: Li, Bo; Wu, Tong; Cai, Bo; Zhang, Lixian; Chen, Hong
• 署名单位: Harbin Institute of Technology; Tongji University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3677766
• 发表日期: 2026
• 关键词: model-predictive control; adaptive-control; STABILITY

摘要

This article is concerned with the design of online data-driven model predictive control (MPC) for switched linear systems with online-generated subsystems and unknown switching signals. The online input-state data are used to detect switching instants and update the MPC law directly to match the new subsystem bypassing identifying its model. Once a switching is detected, a fast-to-slow excitation scheme will be executed until a suitable MPC law is attainable. Specifically, the fast-sampling excitation is applied for the stabilizing controller design, thereby shortening the inevitable phase of data deficiency. This is followed by a slow-sampling stage to collect sufficient data for updating MPC law. Compared with existing online excitation strategies, the proposed method avoids excessive growth of state trajectories and degrades the risk of constraints violation. Based on set-theory-based techniques, it is revealed that the proposed online data-driven MPC approach theoretically ensures persistent feasibility and stability, provided that the switching is sufficiently slow. Illustrative examples, including an aerospace application, are provided to demonstrate the effectiveness and potential of the theoretical results.