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

职业：调度非线性网络中的驱动传感和控制节点及其在无燃料能源系统中的应用

• 批准号: 2044430
• 负责人: Ahmad Taha
• 金额: $ 52.65万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044430&HistoricalAwards=false
• 关键词: CAREER; Scheduling; Driving; Sensing; Control

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.

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。这些网络在时间上呈非线性发展：系统输入的微小变化会导致感知输出的不成比例的变化（例如，轻微的车祸可能会导致交通中断数小时）。这些系统形成了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表和水泵，高速公路上的坡道仪表和摄像头，以及能源网络中的太阳能电池板和智能电表。因此，传感器和控制器的调度是保证各种系统和城市基础设施可靠运行的重要一步。传感器和控制器放置的时间和位置仍然是该领域专家面临的具有挑战性的问题。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，该学院早期职业发展计划（Career）奖支持基础研究，以了解这一计划。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。这是从现有文献的转变，而不是关注非线性动力学的线性近似，这导致在各种应用中表现平平。此外，在能源系统脱碳的迫切环境需求的推动下，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性原理，以及有效的二进制搜索方法。这些基础将导致生成驱动控制/传感节点的子集，并在识别漏洞和网络故障方面具有重要意义。该研究还将创建可扩展的优化方法，这些方法被赋予理论属性，使其适用于离线/在线放置或调度传感和控制节点，适用于不同规模的网络，适用于通过微分代数或常微分方程建模的系统，适用于存在或不存在良性或恶意不确定性的情况。该项目将应用无燃料电力网络的理论基础，该网络的特点是可再生能源和消费者行为的间歇性。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound

• DOI: 10.1016/j.automatica.2021.109904
• 发表时间: 2020-12
• 期刊: Autom.
• 作者: Sebastian A. Nugroho;A. Taha

Load- and Renewable-Following Control of Linearization-Free Differential Algebraic Equation Power System Models

• DOI: 10.1109/tcst.2023.3244492
• 发表时间: 2021-04
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Sebastian A. Nugroho;A. Taha

Control Node Selection Algorithm for Nonlinear Dynamic Networks

• DOI: 10.1109/lcsys.2020.3019591
• 发表时间: 2020-08
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: A. Haber;Sebastian A. Nugroho;Patricio Torres;A. Taha

Observers for Differential Algebraic Equation Models of Power Networks: Jointly Estimating Dynamic and Algebraic States

电力网络微分代数方程模型的观测器：联合估计动态和代数状态

• DOI: 10.1109/tcns.2022.3158754
• 发表时间: 2022
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Nugroho, Sebastian Adi;Taha, Ahmad;Gatsis, Nikolaos;Zhao, Junbo
• 通讯作者: Zhao, Junbo

Where Should Traffic Sensors Be Placed on Highways?

交通传感器应该安装在高速公路的什么位置？

• 发表时间: 2021
• 期刊: IEEE transactions on intelligent transportation systems
• 影响因子: 8.5
• 作者: Sebastian A. Nugroho, Suyash C.