引用本文:赵华荣,彭力,于洪年,沈奕宏.考虑量化的多智能体系统数据驱动双向一致性控制[J].控制理论与应用,2022,39(2):336~342.[点击复制]
ZHAO Hua-rong,PENG Li,YU Hong-nian,SHENG Yi-hong.Data-driven bipartite consensus control for multi-agent systems with data quantization[J].Control Theory and Technology,2022,39(2):336~342.[点击复制]
考虑量化的多智能体系统数据驱动双向一致性控制
Data-driven bipartite consensus control for multi-agent systems with data quantization
摘要点击 1465  全文点击 557  投稿时间：2021-01-24  修订日期：2021-05-16
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DOI编号  10.7641/CTA.2021.10087
  2022,39(2):336-342
中文关键词  数据驱动控制  多智能体系统  双向一致性控制  量化控制  无模型自适应控制
英文关键词  data-driven control  multi-agent systems  bipartite consensus control  quantization control  model-free adaptive control
基金项目  国家重点研发项目(2018YFD0400902), 国家自然科学基金项目(61873112)资助
作者单位E-mail
赵华荣 江南大学 zhaohuarong@stu.jiangnan.edu.cn 
彭力* 江南大学 jnpengli@outlook.com 
于洪年 爱丁堡龙比亚大学  
沈奕宏 江南大学  
中文摘要
      针对未知动力学模型非线性离散时间多智能体系统, 在信息传递过程中的数据量化问题, 以及智能体之间 的合作与竞争关系, 提出了一种数据驱动控制算法, 实现了多智能体系统的双向一致性跟踪控制. 首先, 利用紧凑形 动态线性化(CFDL)方法, 将未知动力学模型的非线性智能体转化为含有时变参数的数据模型, 并通过设计性能指 标函数获得时变参数的估计算法; 然后基于该数据模型, 利用代数图论和扇形界算法, 设计了一种量化数据驱动分 布式双向一致性跟踪控制协议, 并对其收敛性给出了严格的证明. 结果表明, 当多智能体系统存在数据量化时, 所 设计的控制协议仍可以保证双向一致性跟踪误差收敛到0. 最后, 通过仿真实验和对比实验, 进一步验证了该控制 协议的有效性和鲁棒性.
英文摘要
      In this paper, we investigate the data quantization problem of an unknown dynamics model of nonlinear discrete-time multi-agent systems (MASs) with collaborative and antagonistic relationships and propose a data-driven control algorithm for MASs to perform bipartite consensus tracking control. We first develop an estimation algorithm of the time-varying parameter by designing a performance index function through transforming the unknown dynamics model nonlinear agent into a data model with a time-varying parameter using the compact form dynamic linearization (CFDL) approach. We then design a quantized data-driven distributed bipartite consensus tracking control protocol based on the data model by employing the algebraic graph theory and the sector-bound approach. We also strictly prove the convergence property of the proposed algorithm. The results show that although the MASs subject to quantized data, the formulated protocol still guarantees the bipartite consensus tracking errors of MASs to converge to zero. Finally, the developed approach’s effectiveness and robustness are further verified through a numerical example and a contrast experiment.