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

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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.