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基于模型预测控制的不确定马尔可夫跳变网络物理系统异步容错控制

MPC-Based Asynchronous Attack Tolerant Control for Uncertain Markov Jump Cyber-Physical Systems.

作者信息

Wang Lanxin, Long Yue, Li Tieshan, Park Ju H

出版信息

IEEE Trans Cybern. 2024 Dec;54(12):7489-7500. doi: 10.1109/TCYB.2024.3430468. Epub 2024 Nov 27.

DOI:10.1109/TCYB.2024.3430468
PMID:39073998
Abstract

The model predictive control (MPC)-based asynchronous attack tolerant control scheme is investigated in this article for uncertain Markov jump cyber-physical systems (MJCPSs) under the Denial-of-Service (DoS) attack. To tackle the problem of the system running mode may not be observed in the control center, an asynchronous model predictive controller is proposed. Specifically, a dynamic controller, which can tune the performance online, is designed besides a traditional state feedback one. Even though such a combination may cause possible degradation of system performance, it can expand the initial feasible region and relieve the online computation burden efficiently. In addition, a decision variable is introduced to alleviate limitations on the feasible region generated by the constraints in the traditional MPC method. A series of solvable optimal problems are further constructed to achieve the desired performances. Finally, an application of the proposed method is given to demonstrate its effectiveness.

摘要

本文研究了基于模型预测控制(MPC)的异步攻击容忍控制方案,用于应对拒绝服务(DoS)攻击下的不确定马尔可夫跳跃网络物理系统(MJCPSs)。为解决控制中心可能无法观测到系统运行模式的问题,提出了一种异步模型预测控制器。具体而言,除了传统的状态反馈控制器外,还设计了一种能够在线调整性能的动态控制器。尽管这种组合可能会导致系统性能的潜在下降,但它可以扩大初始可行域并有效减轻在线计算负担。此外,引入了一个决策变量,以缓解传统MPC方法中约束条件对可行域产生的限制。进一步构建了一系列可解的最优问题,以实现期望的性能。最后,给出了所提方法的一个应用实例,以证明其有效性。

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