Optimal Co-Design of Wireless Resource Management and Multi-Loop Networked Control

无线资源管理与多环网络控制的优化协同设计

• 批准号: 315177489
• 负责人: Professorin Dr.-Ing. Sandra Hirche
• 金额: --
• 依托单位: Lehrstuhl für Kommunikationsnetze (LKN)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/315177489?language=en
• 关键词: Optimal; Co; Design; Wireless; Resource

Physical entities that are controlled or monitored by computational algorithms and supported by a communication network providing information exchange are typically called cyber-physical systems (CPS). Smart grids, autonomous driving and robotics are some of the most prominent examples to CPS. From a system theoretic viewpoint, CPS consist of multiple control loops closed over shared a communication network, also called networked control systems (NCS). In typical NCS scenarios, the control and communication layers strongly affect the performance of each other. Novel flexible and scalable cross-layer solutions are required, which are responsive to the real-time variations of the individual layers. The development of such communication and control strategies is in the focus of our research project. During the first phase of our project in the DFG SPP Cyber Physical Networking (CPN), we have investigated the control and communication co-design as an optimization problem in shared wireless single-hop and two-hop networks with a focus on multiple linear-quadratic regulator (LQR) feedback control loops and medium access control (MAC) layer with a packet erasure channel model. We have developed control and communication policies such that the overall quality-of-control (QoC) is maximized and presented an initial fundamentally novel systematic solution for the optimal co-design of control and MAC policies in NCS. Moreover, in cooperation with partners in the SPP CPN, we have developed a lightweight open source control and communication platform for initial benchmarking.In this second phase of the project, our goal is to solve a more general global optimization problem that finds optimal joint policies for control and communication taking forwarding delay, queuing, and packet loss in a multi-hop network as well as heterogeneous control task criticalities into account. As part of our approach we introduce the novel concept of utility-of-information (UoI). We aim at developing UoI functions that unify the coupling effects, inter-layer dependencies and heterogeneous task requirements in one single function. UoI functions and their approximations support the decomposition of the global problem into local sub-problems that are solved in a decentralized fashion. In the course of our project we will develop a fundamental understanding of the UoI for various control task criticalities and communication contexts of different layers. We develop a framework for multi-hop and multi-loop control networks that analytically models control and network states and corresponding transitions through information exchange. The final goal is to formulate the global optimization problem that maximizes the quality-of-control for multi-loop and multi-hop scenarios and to derive tractable and scalable local policies. We aim to validate our theoretical findings in a practical, proof-of-concept experiment with multiple robots sharing a multi-hop wireless network.

由计算算法控制或监视并由提供信息交换的通信网络支持的物理实体通常被称为网络物理系统（CPS）。智能电网、自动驾驶和机器人是CPS最突出的例子。从系统论的观点来看，CPS是由多个控制回路通过共享的通信网络组成的，也称为网络控制系统（NCS）。在典型的网络控制系统中，控制层和通信层的性能相互影响。需要新的灵活和可扩展的跨层解决方案，这是响应于各个层的实时变化。这种通信和控制策略的发展是我们研究项目的重点。在DFG SPP网络物理网络（CPN）项目的第一阶段，我们研究了共享无线单跳和两跳网络中的控制和通信协同设计优化问题，重点是多个线性二次调节器（LQR）反馈控制环路和介质访问控制（MAC）层与数据包擦除信道模型。我们已经制定了控制和通信政策，使整体质量的控制（QoC）是最大化，并提出了一个初步的根本新颖的系统解决方案，在NCS中的控制和MAC策略的最佳协同设计。此外，我们与SPP CPN的合作伙伴合作，开发了一个轻量级的开源控制和通信平台，用于初始基准测试。在项目的第二阶段，我们的目标是解决一个更一般的全局优化问题，该问题将转发延迟，排队，和多跳网络中的分组丢失以及异构控制任务关键性。作为我们方法的一部分，我们引入了信息效用（UOI）的新概念。我们的目标是开发UoI功能，统一的耦合效应，层间的依赖关系和异构的任务要求在一个单一的功能。UoI函数及其近似支持将全局问题分解为以分散方式解决的局部子问题。在我们的项目过程中，我们将对UoI的各种控制任务关键性和不同层的通信上下文有一个基本的了解。我们开发了一个框架，多跳和多回路控制网络，分析模型的控制和网络状态和相应的过渡，通过信息交换。最终目标是制定全局优化问题，最大限度地提高多环和多跳的情况下的控制质量，并获得易于处理和可扩展的本地政策。我们的目标是验证我们的理论研究结果，在一个实际的，概念验证实验与多个机器人共享一个多跳无线网络。