Finite-Time Resilient Control of Networked Markov Switched Nonlinear Systems: A Relaxed Design

IF 8.7 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Haiyang Chen;Guangdeng Zong;Mouquan Shen;Fangzheng Gao
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引用次数: 0

Abstract

Robust resilient control is promising and effective since it can combat gain perturbations in control system design. In this article, finite-time resilient control is investigated for the networked Markov switched nonlinear systems (NMSNSs). Both additive and multiplicative feedback gain perturbations with randomly occurring manners are considered during the controller design to improve its tolerance of inaccurate controller implementation. The mode information that is partially available to the controller is incorporated into the design. With the help of the fuzzy-logic method, fuzzy resilient output-feedback controllers are established to cope with the gain perturbations and immeasurable system states. Then, by developing a novel switching model, relaxed conditions compared with the existing results are obtained which guarantee the finite-time boundedness (FTB) of NMSNSs. Based on the FTB analysis, a fuzzy design algorithm is proposed via a new separation approach to obtain the controller gains. Eventually, simulations are conducted via a multimode robotic arm system to validate the achieved results.
网络马尔可夫切换非线性系统的有限时间弹性控制:一种松弛设计
鲁棒弹性控制能够有效地对抗控制系统设计中的增益摄动,是一种很有前途的控制方法。本文研究了网络马尔可夫切换非线性系统的有限时间弹性控制问题。在控制器设计中考虑了随机方式的加性和乘性反馈增益摄动,以提高其对不准确控制器实现的容错性。将控制器部分可用的模式信息纳入设计中。针对增益摄动和系统状态不可测的情况,利用模糊逻辑方法建立了模糊弹性输出反馈控制器。然后,通过建立一种新的开关模型,得到了保证NMSNSs具有有限时间有界性的宽松条件。在FTB分析的基础上,提出了一种模糊设计算法,通过一种新的分离方法来获得控制器增益。最后,通过多模态机械臂系统进行了仿真,验证了所获得的结果。
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
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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