Robust Decentralized Control of Large-Scale Networked Systems: Fundamental Limits and Data-Driven Feedbacks

感知和反馈的概念已经成为工程科学、生物系统以及许多社会科学和经济学领域的重要组成部分。这些学科中的网络科学的出现为传感和反馈提供了许多新的机会。与各种传感器的进步和可用性相结合，提供了丰富的有用数据，网络系统提供了允许执行非常复杂任务的能力。网络信息物理系统开发中的一个主要挑战是建立一个统一的数学框架，该框架允许物理系统层与计算层、网络层和潜在的人层集成。控制系统将在任何这样的框架中发挥不可避免的作用。另一个相当大的挑战源于难以获得大型互联控制系统的准确模型，并且需要在获得进一步的感官信息后改进这些模型；从这个意义上说，未来的控制输入需要以数据驱动的方式进行调整。最后，研究所提出的控制体系结构对故障或恶意行为的脆弱性，并通过弹性设计进行预防，是安全关键型网络物理系统的重要组成部分。为了克服这个问题的高度跨学科性质，提议的项目在三个密切相关的主要重点中进行了讨论。第一个要点旨在说明反馈分散所造成的基本限制。在可控性、稳定性和弹性的背景下，用数学方法描述去中心化的缺点是至关重要的。第二个重点是设计健壮的、数据驱动的分布式算法。通过感官观测获得的大量数据为减少系统建模中不可避免的不确定性提供了机会；这导致了数据驱动的解决方案可以实时实施，而关键部分的信息只能在事后才可用。网络控制系统中动态和反馈的存在将这些设置中设计数据驱动算法的挑战与机器学习文献中在线优化的支柱框架区分开来。最后，第三个重点是针对由于反馈分散而产生的性能和安全问题；分布式控制的主要假设是，所有智能体合作以实现全局目标，项目这一部分的目标是调查这些算法在失败、延迟或恶意行为方面的漏洞，并提出鲁棒的解决方案。该项目将支持培养2名博士后、6名博士生、7名硕士和2名usra，并将有助于加强加拿大在这些关键投资领域的地位。

The concepts of sensing and feedback have been essential parts of engineering sciences, biological systems, and many areas of social sciences and economics. The emergence of network sciences within these disciplines has revealed numerous new opportunities for sensing and feedbacks. Integrated with the advancement and availability of a variety of sensors, which supply an abundance of useful data, networked systems have provided capabilities that allow for the execution of tasks that are remarkably complex. One major challenge in the development of networked cyber-physical systems is to establish a unifying mathematical framework that allows for the integration of the physical system layer with computing, networking, and potentially human layers. Control systems will play an inevitable role in any such framework. The other considerable challenge stems from the difficulty of obtaining accurate models for large-scale interconnected control systems, and the need to improve these models upon obtaining further sensory information; in this sense, future control inputs need to be adjusted in a data-driven fashion. Finally, studying the vulnerability of the proposed control architectures to failure or malicious behaviours and prevention through resilient designs is an essential part of safety-critical cyber-physical systems. To overcome the highly interdisciplinary nature of this problem, the proposed project has been discussed in three intimately related main thrusts. The first thrust aims at characterizing fundamental limitations imposed by the decentralization of feedbacks. It is crucial to mathematically characterize the shortcomings of decentralization in the context of controllability, stabilizability, and resilience. The second thrust aims at designing robust and data-driven distributed algorithms. The availability of massive data through sensory observations provides the opportunity for reducing the inevitable uncertainties in system modelling; this leads to data-driven solutions that can be implemented in real-time, with a crucial part of information only available at hindsight. The presence of dynamics and feedback in networked control systems differentiates the challenges in designing data-driven algorithms in these settings from the pillar framework of online optimization in machine learning literature. Finally, the third thrust aims at performance and security issues that arise due to the decentralization of feedbacks; the predominant assumption in distributed control is that all agents cooperate to achieve a global objective, and the goal of this part of the project is to investigate the vulnerabilities of these algorithms with respect to failure, delays, or malicious behaviours, and to propose robust solutions. This program will support the training of 2 postdoctoral researchers, 6 PhD students, 7 MSc students, and 2 USRAs and will contribute to strengthen Canada's position in these key areas of investment.