Event-Triggered Generalized Extended State Observer-Based Control for Nonlinear Networked Systems Under Gain Variation and Multi-Channel Attacks

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-10-20 DOI:10.1002/rnc.7681
Pengcheng Zhang, Jianyu Wang, Yajuan Liu, Sangmoon Lee
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Abstract

This paper investigates the event-triggered generalized extended state observer-based estimation issue for a class of nonlinear networked control systems with unmodeled dynamics, external disturbances and multi-channel attacks. In order to resist different forms of attack threats on multiple communication channels from sensors to the observer, Markov chain is introduced to describe the stochastic switching or jumping behavior between different attack modes. Considering the limited network resources, the measured outputs are transmitted to the observer only when the triggering conditions are met. Moreover, the parameters modeled by the Bernoulli process are adopted to help analyze potential random gain variations of the generalized extended state observer. By employing Lyapunov stability theory and stochastic systems analysis, sufficient conditions are derived to ensure that the augmented system is exponentially bounded in mean square, and the expected observer gains are further determined through linear matrix inequalities. Finally, a numerical example and a simulation related to the RLC series circuit system are conducted, illustrating the proposed method's effectiveness.

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增益变化和多通道攻击下非线性网络系统的事件触发广义扩展状态观测器控制
研究了一类具有未建模动力学、外部干扰和多通道攻击的非线性网络控制系统的基于事件触发的广义扩展状态观测器估计问题。为了抵御从传感器到观测器的多个通信信道上不同形式的攻击威胁,引入马尔可夫链来描述不同攻击模式之间的随机切换或跳跃行为。考虑到有限的网络资源,只有在满足触发条件的情况下,才将被测输出传输到观测器。此外,采用伯努利过程建模的参数帮助分析广义扩展状态观测器的潜在随机增益变化。利用李雅普诺夫稳定性理论和随机系统分析,推导了增广系统在均方上呈指数有界的充分条件,并通过线性矩阵不等式进一步确定了期望观测器增益。最后,对RLC串联电路系统进行了数值算例和仿真,验证了该方法的有效性。
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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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Issue Information Issue Information A Constructive Approach to Multi-Variable Extremum Seeking With Discrete-Time Delayed Noisy Measurements Affine Formation Control for End-Effectors of Networked Manipulators With Maneuvering Leaders and Unknown System Parameters Neural Network-Observer-Based ILC of Nonlinear Systems With Event-Driven Strategy
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