Data-Driven Resilient Predictive Control Under Denial-of-Service

• 成果类型: Article
• 署名作者: Liu, Wenjie; Sun, Jian; Wang, Gang; Bullo, Francesco; Chen, Jie
• 署名单位: Beijing Institute of Technology; Beijing Institute of Technology; Beijing Institute of Technology; University of California System; University of California Santa Barbara; University of California System; University of California Santa Barbara; Tongji University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3209399
• 发表日期: 2023
• 页码: 4722-4737
• 关键词: Index Terms-Data-driven control; denial-of-service at-tack; input-to-state stability; model predictive control

摘要

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on precollected input-output trajectories (i.e., without explicit system models). In this article, we take an initial step toward data-driven stabilization of LTI systems under DoS attacks, and develop a resilient model predictive control scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.