Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. In controlled self-organization , which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs.

中文翻译：

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控制大规模自组织网络的分层最优控制方法

自组织具有高可扩展性、适应性、灵活性和鲁棒性的潜力，这是实现未来网络的重要特征。然而，与使用全局信息的传统确定性系统的控制相比，自组织控制的收敛在某些实际应用中很慢。因此，促进自组织控制的收敛是很重要的。在受控自组织，将外部控制器引入自组织系统，控制网络以将系统引导到所需状态。尽管现有的受控自组织方案可以达到相同的状态，但外部控制器很难收集有关网络的信息并向网络提供控制输入，尤其是在网络规模很大的情况下。这是因为设计外部控制器和计算控制输入的计算成本随着网络中节点数量的增加而迅速增加。因此，我们将网络划分为几个子网络，并引入两种类型的控制器，一个中央控制器和几个以分层方式控制网络的子控制器。在这项研究中，我们提出了一种用于自组织系统的分层最优反馈机制，并将该机制应用于基于势的自组织路由。仿真结果表明，所提出的机制将势场构建（即路线构建）的收敛速度提高了 10.6 倍，且计算和通信成本低。