Sliding Mode Control With Adaptive Quantizer's Parameters for Markov Jump Systems Under DoS Attacks

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xintong Xie, Bei Chen
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引用次数: 0

Abstract

Under the digital communication channel, signal quantization will complicate the design and analysis of controlled systems due to quantization errors. In most of the existing work, the controller design only focuses on the robustness of the controlled system, that is, the performance of the system in the presence of quantization errors, without considering whether the effects of the quantization process can be eliminated, which leads to conservatism and limits the improvement of control performance. Therefore, in this work, a sliding mode controller is designed for Markov jump systems subject to denial-of-service attacks, without considering quantization. Then, under the quantization case, the same control performance can still be achieved based on the existing sliding mode controller by designing an online adjustment strategy for the quantizer's parameters. Using the Lyapunov theory, the exponential ultimate boundedness of the system and the reachability of the sliding surface are analyzed respectively. Finally, the simulation results illustrate the proposed strategy.

DoS攻击下马尔可夫跳跃系统的自适应量化参数滑模控制
在数字通信信道下,由于量化误差的存在,信号量化会使被控系统的设计和分析复杂化。在现有的大部分工作中,控制器设计只关注被控系统的鲁棒性,即系统在存在量化误差时的性能,而没有考虑量化过程的影响能否消除,从而导致保守性,限制了控制性能的提高。因此,本文设计了一种滑模控制器,用于马尔可夫跳变系统的拒绝服务攻击,而不考虑量化。然后,在量化情况下,通过设计量化器参数的在线调整策略,在现有滑模控制器的基础上仍然可以获得相同的控制性能。利用李雅普诺夫理论,分别分析了系统的指数极限有界性和滑动面的可达性。最后,仿真结果验证了所提出的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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