Event-triggered stochastic model predictive control based on distributionally robust optimization approach for network control systems under DoS attacks
IF 3.4 2区 工程技术Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
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引用次数: 0
Abstract
In this paper, we propose a novel event-triggered stochastic model predictive control (SMPC) algorithm based on distributionally robust optimization (DRO) for network control systems (NCSs) that are affected by additive disturbances and Denial-of-Service (DoS) attacks. The event-triggered mechanism is designed to reduce sampling times, communication costs, and computational demands while maintaining performance. By converting chance constraints into second-order cone (SOC) constraints, the algorithm effectively addresses disturbances with limited prior knowledge, utilizing accessible first and second moments. The proposed event-triggered DR-SMPC (ETM-DR-SMPC) has longer sampling intervals than periodic sampling DR-SMPC (PDR-SMPC). Compared to self-triggered SMPC (SSMPC), ETM-DR-SMPC has longer sampling intervals, less computation, better performance and fewer constraint violations. It also has better performance and longer sampling intervals than event-triggered robust model predictive control (ETM-RMPC). Furthermore, we demonstrate the exponential mean square stability of ETM-DR-SMPC for the first time and the recursive feasibility of ETM-DR-SMPC is guaranteed. Numerical simulations validate the effectiveness of the proposed algorithm.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.