Zhao Huarong, Peng Li, Yu Hongnian
Research Center of Engineering Applications for IOT, Jiangnan University, Wuxi 214122, China.
Jiangsu Province Internet of Things Application Technology Key Construction Laboratory, Wuxi Taihu College, Wuxi 214145, China.
Sensors (Basel). 2020 Jul 27;20(15):4164. doi: 10.3390/s20154164.
This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input-output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.
本文提出了一种分布式无模型自适应二分共识跟踪（DMFABCT）方案。该方案无需精确的数学模型，但能够实现具有切换拓扑和合作竞争网络的未知动态离散时间异构多智能体系统（MAS）的二分不变时和时变轨迹跟踪。该算法的主要创新点在于通过伪偏导数（PPD）方法估计等效动态线性化数据模型，该方法仅需要每个智能体的输入输出（I/O）数据，并研究了智能体之间的协作交互。给出了DMFABCT收敛性的严格证明，结果表明可以减小轨迹误差。最后，三个仿真结果表明，新颖的DMFABCT方案对于具有切换拓扑的未知异构离散时间MAS完成二分共识跟踪任务是有效且鲁棒的。
