CAREER: Scheduling Driving Sensing and Control Nodes in Nonlinear Networks with Applications to Fuel-Free Energy Systems

电力和供水系统、互联网和其他基础设施被组装成相互交织的网络。这些网络随时间非线性地演变：系统输入的微小变化会导致感测输出的不成比例的变化（例如，一场小车祸会使交通中断数小时）。这些系统形成依赖于无处不在的传感器和控制器的非线性网络。例子包括供水系统中的水流量计和水泵、高速公路上的匝道仪表和摄像头，以及能源网络中的太阳能电池板和智能仪表。因此，传感器和控制器的调度体现了各种系统和城市基础设施可靠运行的重要一步。传感器和控制器的放置时间和位置仍然是该领域专家面临的挑战性问题。这些不仅仅是工程问题，总的来说，它们是社会经济上的当务之急。为此，这个教师早期职业发展计划（CAREER）奖支持基础研究，以了解这种调度。该项目通过为非线性系统中的传感器和控制器调度问题创建创新算法和教育工具，为科学进步提供了机会。这是现有文献的一个转变，现有文献侧重于非线性动力学的线性近似，这导致各种应用的性能不佳。此外，出于对能源系统脱碳的迫切环境需求，探索了可再生能源的应用。 该项目还将（a）通过开源计算创建众包教育材料;（B）通过吸引来自代表性不足群体的学生参与课程开发。该项目为探索非线性网络中的传感器和控制器调度问题提供了一个新的框架，为动态网络科学提供了理论突破。特别是，该项目将研究有效量化可观测性和可控性的新方法，同时探索非线性动力学的参数化，控制和网络科学的第一原理以及有效的二进制搜索方法。这些基础将导致生成驾驶控制/传感节点的子集，并在识别漏洞和网络故障方面产生重大影响。该研究还将创建可扩展的优化方法，这些方法具有理论特性，使其适合于传感和控制节点的离线/在线放置或调度，适用于不同大小的网络，适用于通过微分代数或常微分方程建模的系统，以及存在或不存在良性或恶意的不确定性。该项目将在以可再生能源的间歇性和消费者行为为特征的无燃料电力网络中应用理论基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Power and water systems, the Internet, and other infrastructure are assembled into intertwined networks. These networks evolve nonlinearly in time: small changes in a system’s inputs result in disproportionate changes in the sensed outputs (e.g., a minor car accident can disrupt traffic for hours). These systems form nonlinear networks that rely on ubiquitous sensors and controllers. Examples include water flow meters and pumps in water systems, ramp meters and cameras on highways, and solar panels and smart meters in energy networks. As a result, the scheduling of sensors and controllers embodies a major step in the reliable operation of various systems and urban infrastructure. When and where sensors and controllers are placed still present challenging questions for experts in this field. These are not merely engineering questions---they are, by and large, socio-economically imperative. To that end, this Faculty Early Career Development Program (CAREER) award supports fundamental research to understand this scheduling. This project presents an opportunity for scientific advancements by creating innovative algorithms and educational tools for the sensors and controllers scheduling problem in nonlinear systems. This is a shift from existing literature that focuses instead on linear approximations of nonlinear dynamics, which result in underwhelming performance for various applications. Furthermore, motivated by the urgent environmental need to decarbonize energy systems, applications to the placement of renewable energy resources are explored. The project will also (a) create crowdsourced educational material through open-source computing and (b) build on curriculum development through engaging students from under-represented groups.The project offers a novel framework for the exploration of the sensors and controllers scheduling problem in nonlinear networks, offering theoretical breakthroughs in dynamic network sciences. In particular, the project will investigate new approaches to efficiently quantify observability and controllability, while exploring parameterization of nonlinear dynamics, first principles in control and network science, and efficient binary search methods. The foundations will lead to generating subsets of driving control/sensing nodes and have major implications in identifying vulnerabilities and network failures. The research will also create scalable optimization methods that are endowed with theoretical properties that make them suitable for offline/online placement or scheduling of sensing and control nodes, for networks of varying sizes, for systems modeled via differential algebraic or ordinary differential equations, and under the presence or absence of benign or malicious uncertainty. The project will apply the theoretical foundations in fuel-free power networks that are characterized by the intermittent nature of renewables and consumer behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.