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Network-based passive estimation for switched complex dynamical networks under persistent dwell-time with limited signals

机译:基于网络的基于网络的被动估计,其具有有限信号持久性停留时间下的交换机动态网络

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摘要

In this paper, the state estimation issue for a set of switched complex dynamic networks affected by quantization is studied, in which the switching process is assumed to follow persistent dwell-time switching regulation. Thereinto, the switching regulation aforementioned describes the switchings among different parameters on complex dynamic networks. Meanwhile, for the network-based model, in the communication channels from the sensor to the estimator, quantization is inevitable to be taken into consideration. To track partially inaccessible information in the target system, a state estimator is thoroughly reconstructed. Intensive attention is that a set of sufficient conditions can be derived by using some simple matrix transformation methods, linear matrix inequality and Lyapunov stability theory, to further assure the error dynamic obtained is globally uniformly exponentially stable and meets passive property. The serviceability of the state estimator gains solved is finally verified and the effectiveness of the proposed design approach is further illustrated. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:在本文中,研究了由量化影响的一组交换机复杂动态网络的状态估计问题,其中假设开关过程遵循持久停留时间切换调节。其中,前面的开关调节描述了复杂动态网络上不同参数之间的交换。同时,对于基于网络的模型,在从传感器到估计器的通信信道中,量化是不可避免的。要跟踪目标系统中的部分无法访问的信息,请彻底重建状态估计器。密集的关注是,通过使用一些简单的矩阵变换方法,线性矩阵不等式和Lyapunov稳定性理论可以推导出一组足够的条件,进一步确保所获得的误差动态全球均匀地稳定并满足被动性质。最终验证了所解决的状态估计增益的可维护性,并进一步说明了所提出的设计方法的有效性。 (c)2020富兰克林学院。 elsevier有限公司出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2020年第15期|10921-10936|共16页
  • 作者单位

    Anhui Univ Technol Sch Met Engn AnHui Prov Key Lab Special Heavy Load Robot Maanshan 243002 Peoples R China;

    Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China;

    Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China;

    Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Peoples R China;

    Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China;

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  • 入库时间 2022-08-18 21:04:29

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