Leader-Following Sampled-Data Consensus of Multiagent Systems with Successive Packet Losses and Stochastic Sampling

被引:0
|
作者
Sun, Hao-Yuan [1 ]
Han, Hong-Gui [1 ]
Sun, Jian [2 ]
Qiao, Jun-Fei [1 ]
机构
[1] Beijing Artificial Intelligence Institute, Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, School of Information Science and Technology, Beijing,100021, China
[2] Beijing Institute of Technology, Beijing,100081, China
关键词
Packet loss;
D O I
10.1109/TCYB.2024.3466610
中图分类号
学科分类号
摘要
In practical applications, sampled-data systems are often affected by unforeseen physical constraints that cause the sampling interval to deviate from the expected value and fluctuate according to a certain probability distribution. This probability distribution can be determined in advance through statistical analysis. Taking into account this stochastic sampling interval, this article focuses on addressing the leader-following sampled-data consensus problem for linear multiagent systems (MASs) with successive packet losses. First, the relationship of the equivalent sampling interval between two successive update instants is established, taking into account the double randomness introduced by both SPLs and stochastic sampling. Then, the equivalent discrete-time MAS is obtained, and the overall leader-following consensus problem is formulated as a stochastic stability problem of the equivalent system by incorporating the sampled-data consensus protocol and properties of the Laplacian matrix. Based on the equivalent the discrete-time system, a consensus criterion is derived under a directed graph by using the Lyapunov theory and leveraging a vectorization technique. By the introduction of a matrix reconstruction approach, the mathematical expectation of a product of three matrices, including the system matrix and its transpose, can be determined. Then, the consensus protocol gain is designed. Finally, an example is provided to validate our theoretical results. © 2024 IEEE.
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收藏
页码:7381 / 7391
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