Network-Informed Control - Control-Informed Network: towards multi techNology dynamICally ChangIng networks(NICCI^2)

网络通知控制 - 控制通知网络：走向多技术动态改变网络（NICCI^2）

• 批准号: 315248657
• 负责人: Professor Dr.-Ing. Falko Dressler
• 金额: --
• 依托单位: Fachgebiet Telekommunikationsnetze (TKN)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/315248657?language=en
• 关键词: Network; Informed; Control; towards; multi

The opportunities provided by feedback control of networked dynamical systems are enormous. However, it is by no means clear how to harness modern wireless communication, network and computation technologies to develop high-performance cyber-physical systems. The main stumbling blocks stem from the significant gaps which exist between understanding of constituent parts and the challenges faced when bringing them together. One of the key complications is that the traffic demands of the control loops is often considered independently from the communication capabilities. In current setups, the control system has to simply cope with the quality of service provided by the network. Whilst this facilitates an independent design of communication and control systems, the achievable performance is limited. Hence, mostly slow and non safety-critical tasks are currently performed via wireless communication.These limitations could be overcome by an efficiently integrated overall optimization of control and com- munication systems. Unfortunately, a practical solution to this optimization problem is infeasible due to the complexity and distributed nature of the overall system. We strive instead for a modular solution. Controllers and network resource management are designed separately, yet exchange information and interact collabora- tively. This exchange comprises predictions of control requirements, communication capabilities and techno- logical choices. The developed algorithms at both the control and communication resource management sides, leverage predictions and learn from their respective actions.Within the first funding phase of this project, we analyzed limits of achievable performance of this class of networked cyber-physical systems. Using suitable deterministic and also stochastic frameworks, we were able to establish minimum requirements on the network, which are necessary to achieve the desired quality of control. We further proposed a number of control-aware scheduling and network-aware control methods wherein information between network manager and controller is exchanged sporadically. Our insights motivate numerous interesting and relevant research questions to be investigated within a second funding period.In the second funding phase of the project, we aim to further elucidate fundamental tradeoffs and develop modular approaches for the design of networked cyber-physical systems. We will focus on practical cases where heterogeneous and complementary wireless communication technologies with time and state-dependent properties are available. Integrating such technologies into possibly large-scale cyber-physical systems with feedback is highly non-trivial. To be successful, we will fuse channel predictions, machine learning and esti- mation techniques with optimization-based methods for scheduling and control. We will exemplarily test and refine our algorithms on networked vehicle systems and industry 4.0 test scenarios with robots.

网络动态系统的反馈控制提供了巨大的机会。然而，如何利用现代无线通信、网络和计算技术来开发高性能的网络物理系统，目前还不清楚。主要的障碍来自对各组成部分的理解与将它们结合在一起时所面临的挑战之间存在的巨大差距。其中一个关键的复杂性是，控制回路的流量需求往往被认为是独立于通信能力。在当前的设置中，控制系统必须简单地科普由网络提供的服务质量。虽然这有利于通信和控制系统的独立设计，但可实现的性能是有限的。因此，目前大多数缓慢和非安全关键的任务是通过无线通信来执行的。这些限制可以通过控制和通信系统的有效集成的整体优化来克服。不幸的是，这个优化问题的实际解决方案是不可行的，由于整个系统的复杂性和分布式的性质。相反，我们致力于模块化解决方案。控制器和网络资源管理是分开设计的，但交换信息和相互协作。这种交换包括对控制要求、通信能力和技术选择的预测。在控制和通信资源管理方面开发的算法，利用预测并从各自的行动中学习。在该项目的第一个资助阶段，我们分析了这类网络化网络物理系统可实现性能的限制。使用合适的确定性和随机框架，我们能够建立对网络的最低要求，这是必要的，以实现所需的控制质量。我们进一步提出了一些控制感知调度和网络感知控制方法，其中网络管理器和控制器之间的信息是偶尔交换的。我们的见解激发了许多有趣的和相关的研究问题，将在第二个资助期内进行调查。在该项目的第二个资助阶段，我们的目标是进一步阐明基本的权衡，并开发用于网络化网络物理系统设计的模块化方法。我们将专注于实际情况下，异构和互补的无线通信技术与时间和状态相关的属性。将这些技术集成到可能具有反馈的大规模网络物理系统中是非常重要的。为了取得成功，我们将把渠道预测、机器学习和估计技术与基于优化的调度和控制方法融合在一起。我们将在联网车辆系统和机器人工业4.0测试场景中测试和改进我们的算法。