This paper aims to solve the problem of the coordination of cloud-based model-free multiagent systems (CBMF-MASs) with communication constraints. To improve the data processing ability of multiagent systems for massive real-time data and decrease the communication burden of individual agents, cloud computing systems are utilized to establish the multiagent system. To actively compensate for network delays and data losses in all communication channels and coordinate the output of the CBMF-MAS, a novel data-driven networked distributed predictive control method (DDNDPC) is presented, which is independent of system's structure model and only relies on system's input and output data. Furthermore, the stability and consensus criterion of the CBMF-MAS are established by proposing a simultaneous analysis approach for the stability and consensus. Finally, the effectiveness and practicality of the DDNDPC method are verified through numerical simulations and cloud-based experimental tests. The achievements promote the application of large-scale multiagent systems in practice.

中文翻译：

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具有通信约束的基于云的无模型多智能体系统的数据驱动分布式协调控制


本文旨在解决具有通信约束的基于云的无模型多智能体系统（CBMF-MAS）的协调问题。为了提高多Agent系统对海量实时数据的处理能力，减轻各个Agent的通信负担，利用云计算系统建立多Agent系统。为了主动补偿所有通信通道中的网络延迟和数据丢失并协调CBMF-MAS的输出，提出了一种新颖的数据驱动的网络化分布式预测控制方法（DDNDPC），该方法独立于系统结构模型，仅依赖于系统的输入和输出数据。此外，通过提出稳定性和一致性的同时分析方法，建立了CBMF-MAS的稳定性和一致性标准。最后，通过数值模拟和基于云的实验测试验证了DDNDPC方法的有效性和实用性。该成果促进了大规模多智能体系统在实践中的应用。