Analysis and Control of Emergent Dynamics in Multilayered Networks of Dynamical Systems

动力系统多层网络中突现动力学的分析与控制

• 批准号: RGPIN-2018-06363
• 负责人: Scardovi, Luca
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712981
• 关键词: Analysis; Control; Emergent; Dynamics; Multilayered

Some of the most challenging scientific questions of the modern age involve the understanding of networks of interconnected systems. In applications, networks are typically open to the external environment and they interact with other networks across multiple time and spatial scales. For example, the brain is itself a network of subnetworks in constant interaction, both internally (between each other) and externally (that is, with sensory inputs and motor outputs). Similar modular structures are also common in the engineering world: coupled social networks (e.g., Facebook, Twitter); multimodal transportation networks composed of different layers (e.g., bus, subway); heterogeneous multi-robot networks (e.g., flying drones, underwater vehicles), are typical examples. We will call these networks "multilayer networks". Our research program has two main long term goals: i) to provide a theoretical framework to certify dynamical properties of multilayer networks ; ii) To develop a theory to control their (emergent) dynamics. In the short term we will apply our framework to help understanding how synchronized rhythmic activity emerges in neuronal networks and to design distributed control strategies to coordinate heterogeneous teams of underactuated vehicles. The integration of graph theoretical tools into a control theoretical framework is at the core of our methodology. In the long term, the results of our research can be used in a range of applications that are of vital importance to Canada and have significant impact in scientific, commercialization, economic, and societal domains. Those include surveillance and monitoring (e.g., underwater, maritime, ground, air, and space applications), biology (cellular biology, neuroscience), and biomedical systems (e.g., the development of neurostimulation devices for the treatment of neurological diseases).

现代一些最具挑战性的科学问题涉及对互联系统网络的理解。 在应用中，网络通常对外部环境是开放的，并且它们跨多个时间和空间尺度与其他网络交互。例如，大脑本身就是一个不断相互作用的子网络，内部（彼此之间）和外部（即感官输入和运动输出）。类似的模块化结构在工程世界中也很常见：耦合的社交网络（例如，Facebook，Twitter）;由不同层组成的多式联运网络（例如，公共汽车、地铁）;异构多机器人网络（例如，无人驾驶飞机、水下航行器）就是典型的例子。我们将这些网络称为“多层网络”。 我们的研究计划有两个主要的长期目标：i）提供一个理论框架来证明多层网络的动力学性质; ii）开发一个理论来控制它们的（涌现）动力学。在短期内，我们将应用我们的框架，以帮助理解如何同步的节奏活动出现在神经网络和设计分布式控制策略，以协调异构团队的欠驱动车辆。将图论工具整合到控制理论框架中是我们方法论的核心。 从长远来看，我们的研究成果可以用于对加拿大至关重要的一系列应用，并在科学，商业化，经济和社会领域产生重大影响。这些措施包括监督和监测（例如，水下、海上、地面、空中和空间应用），生物学（细胞生物学，神经科学），和生物医学系统（例如，用于治疗神经疾病的神经刺激装置的开发）。