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一类具有数据包丢失的未知异构非线性多智能体系统的无模型自适应一致性设计

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts.

作者信息

Ren Ye, Liu Shida, Li Deli, Zhang Dongxu, Lei Ting, Wang Li

机构信息

School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, People's Republic of China.

Division of Optical Communications, China Mobile Group Design Institute Co. Ltd., Beijing, 100080, People's Republic of China.

出版信息

Sci Rep. 2024 Oct 4;14(1):23093. doi: 10.1038/s41598-024-73959-8.

DOI:10.1038/s41598-024-73959-8
PMID:39367072
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11452640/
Abstract

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

摘要

本文研究了一类具有随机丢包的未知异构非线性多智能体系统通过网络的一致性问题。基于动态线性化技术，针对无领导者和有领导者跟随两种情况，设计了具有数据补偿机制的新型无模型自适应一致性协议。该方法的优点是在协议设计中仅需要智能体的邻域输入和输出数据。对于稳定性分析，开发了一种基于新的夹逼定理的方法来推导理论结果，而不是在无模型自适应控制中使用的传统压缩映射原理。结果表明，如果通信拓扑是强连通的，那么在无领导者和有领导者跟随两种情况下都能实现一致性。最后，给出了验证理论结果正确性的数值模拟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/89ff0e405649/41598_2024_73959_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/89ff0e405649/41598_2024_73959_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/1d19f82f1258/41598_2024_73959_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/f3d41a8a1409/41598_2024_73959_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/f6d30f446060/41598_2024_73959_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/95a8dec8a726/41598_2024_73959_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/260f9762f68b/41598_2024_73959_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/6a7e7bd3abf6/41598_2024_73959_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/26cec215682f/41598_2024_73959_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/96990bd98172/41598_2024_73959_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/311de8db1876/41598_2024_73959_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/3369e9746e71/41598_2024_73959_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/0c28c756c2c8/41598_2024_73959_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/12e971f7bcbe/41598_2024_73959_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/11452640/89ff0e405649/41598_2024_73959_Fig13_HTML.jpg

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IEEE Trans Neural Netw Learn Syst. 2023 Aug;34(8):4120-4129. doi: 10.1109/TNNLS.2021.3123235. Epub 2023 Aug 4.
2
Distributed Model-Free Adaptive Control for Learning Nonlinear MASs Under DoS Attacks.基于丢包攻击的非线性 MASs 学习的分布式无模型自适应控制。
IEEE Trans Neural Netw Learn Syst. 2023 Mar;34(3):1146-1155. doi: 10.1109/TNNLS.2021.3104978. Epub 2023 Feb 28.
3
Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault.
具有传感器故障的未知MIMO非线性离散时间多智能体系统的数据驱动编队控制
IEEE Trans Neural Netw Learn Syst. 2022 Dec;33(12):7728-7742. doi: 10.1109/TNNLS.2021.3087481. Epub 2022 Nov 30.
4
Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy.基于无模型自适应预测控制策略的云计算系统中多智能体系统的分布式群体协调
IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3461-3473. doi: 10.1109/TNNLS.2021.3053016. Epub 2022 Aug 3.
5
A Physiological Control System for an Implantable Heart Pump That Accommodates for Interpatient and Intrapatient Variations.一种用于可植入心脏泵的生理控制系统，可适应患者间和患者内的变化。
IEEE Trans Biomed Eng. 2020 Apr;67(4):1167-1175. doi: 10.1109/TBME.2019.2932233. Epub 2019 Jul 31.
6
Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control.基于模型自由自适应控制的数据驱动多智能体系统共识跟踪。
IEEE Trans Neural Netw Learn Syst. 2018 May;29(5):1514-1524. doi: 10.1109/TNNLS.2017.2673020. Epub 2017 Mar 14.
7
Dual RBFNNs-Based Model-Free Adaptive Control With Aspen HYSYS Simulation.基于 Dual RBFNNs 的无模型自适应控制与 Aspen HYSYS 仿真。
IEEE Trans Neural Netw Learn Syst. 2017 Mar;28(3):759-765. doi: 10.1109/TNNLS.2016.2522098. Epub 2016 Feb 19.
8
Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach.基于有向图的多智能体系统分布式协同最优控制：逆最优方法。
IEEE Trans Cybern. 2015 Jul;45(7):1315-26. doi: 10.1109/TCYB.2014.2350511. Epub 2014 Sep 9.
9
Second-order global consensus in multiagent networks with random directional link failure.具有随机方向链路故障的多智能体网络中的二阶全局一致性。
IEEE Trans Neural Netw Learn Syst. 2015 Mar;26(3):565-75. doi: 10.1109/TNNLS.2014.2320274. Epub 2014 May 16.
10
Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.一类多输入多输出非线性离散时间系统的数据驱动无模型自适应控制
IEEE Trans Neural Netw. 2011 Dec;22(12):2173-88. doi: 10.1109/TNN.2011.2176141. Epub 2011 Nov 30.