Collaborative Research: CPS: Medium: ASTrA: Automated Synthesis for Trustworthy Autonomous Utility Services

合作研究：CPS：媒介：ASTrA：值得信赖的自治公用事业服务的自动合成

• 批准号: 2139781
• 负责人: Yasser Shoukry
• 金额: $ 33.5万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139781&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; ASTrA

Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts. The proposed framework, termed Automated Synthesis for Trustworthy Autonomous Utility Services (ASTrA), addresses the design challenges via a three-pronged approach. It uses population games to model the effect of distributed decision-making infrastructures (DMI) on large populations of strategic agents. DMIs will be realized via dedicated networked hybrid hardware architectures and algorithms we seek to design. ASTrA further introduces a systematic, layered methodology to automate the design, verification, and validation of DMIs from expressive representations of the requirements. Finally, it offers a set of cutting-edge computational tools to facilitate our methodology by enabling efficient reasoning about the interaction between discrete models, e.g., used to describe complex missions or embedded software components, and continuous models used to describe physical processes. The evaluation plan involves experimentation on a real testbed designed for zero-net-energy applications.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.

具有社会相关性的大型系统（例如发电系统）越来越能够利用新技术来减轻其对环境的影响，例如通过从可再生能源中获取能量。该 NSF CPS 项目旨在研究方法和计算工具，以设计一种新的以用户为中心的能源分配和分配范例，更广泛地说，为值得信赖的公用事业服务设计。在这种范式中，分布式网络系统将协助电力最终用户安排和分配其消耗。此外，它们将使地方和国家公用事业管理者能够优化绿色能源的使用，同时减轻间歇性的影响，促进公平、公正和可负担性。该项目寻求一种易于处理的方法来解决建模和设计这些大规模、混合自治、多代理 CPS 的挑战。其智力优势包括用于设计分布式决策策略和系统架构的新的可扩展方法、算法和工具，可以帮助最终用户实现其目标，同时保证符合设计的公平性、可靠性和物理约束。更广泛的影响包括实现分布式 CPS 的自动化设计，以协调从机器人群到智能制造和智能城市等许多应用中的决策。研究成果还将用于 K-12 和本科生 STEM 推广工作。所提出的框架称为“可信赖自治公用事业服务自动综合”(ASTrA)，通过三管齐下的方法解决了设计挑战。它使用人口博弈来模拟分布式决策基础设施 (DMI) 对大量战略代理的影响。 DMI 将通过我们寻求设计的专用网络混合硬件架构和算法来实现。 ATrA 进一步引入了一种系统的、分层的方法，可以根据需求的表达来自动化 DMI 的设计、验证和确认。最后，它提供了一套尖端的计算工具，通过对离散模型（例如用于描述复杂任务或嵌入式软件组件的离散模型）与用于描述物理过程的连续模型之间的交互进行有效推理来促进我们的方法。该评估计划涉及在专为零净能源应用而设计的真实测试台上进行实验。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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

NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems

NNSynth：神经网络引导的随机系统基于抽象的控制器综合

• DOI: 10.1109/cdc51059.2022.9993158
• 发表时间: 2022-12
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC
• 作者: Sun, Xiaowu;Shoukry, Yasser
• 通讯作者: Shoukry, Yasser

Polynomial-Time Reachability for LTI Systems With Two-Level Lattice Neural Network Controllers

具有两级晶格神经网络控制器的 LTI 系统的多项式时间可达性

• 发表时间: 2022-12
• 期刊: IEEE control systems letters
• 影响因子: 3
• 作者: Ferlez; James and
• 通讯作者: James and

SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge

SEO：边缘多传感器神经控制器的安全意识能源优化框架

• DOI: 10.1109/dac56929.2023.10247751
• 发表时间: 2023-07
• 期刊: IEEE
• 作者: Odema, Mohanad;Ferlez, James;Shoukry, Yasser;Al Faruque, Mohammad Abdullah
• 通讯作者: Al Faruque, Mohammad Abdullah

EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency

EnergyShield：可证明安全的神经网络控制器卸载以提高能源效率

• DOI: 10.1145/3576841.3585935
• 发表时间: 2023-05
• 期刊: ACM
• 作者: Odema, Mohanad;Ferlez, James;Vaisi, Goli;Shoukry, Yasser;Al Faruque, Mohammad Abdullah
• 通讯作者: Al Faruque, Mohammad Abdullah

PolyARBerNN: A Neural Network Guided Solver and Optimizer for Bounded Polynomial Inequalities

PolyARBerNN：神经网络引导的有界多项式不等式求解器和优化器

• DOI: 10.48550/arxiv.2204.05365
• 发表时间: 2022-04-11
• 期刊: ArXiv
• 作者: Wael Fatnassi;Yasser Shoukry
• 通讯作者: Yasser Shoukry