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，如发电系统，越来越能够利用新技术来减轻其环境影响，例如，通过从可再生能源中获取能量。这个NSF CPS项目旨在研究方法和计算工具，以设计一个新的以用户为中心的能源分配和分配模式，更广泛地说，为值得信赖的公用事业服务。在这种模式下，分布式网络系统将帮助电力的最终用户调度和分配他们的消费。此外，它们将使地方和国家公用事业管理人员能够优化绿色能源的使用，同时减轻不稳定的影响，促进公平、公正和可负担性。该项目追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。智能的优点包括新的可扩展的方法，算法和工具的分布式决策策略和系统架构的设计，可以帮助最终用户在满足他们的目标，同时保证符合公平性，可靠性和物理约束的设计。更广泛的影响包括实现分布式CPS的自动化设计，这些CPS在许多应用中协调决策，从机器人群到智能制造和智能城市。研究成果也将用于K-12和本科STEM外展工作。建议的框架，称为自动合成值得信赖的自治公用事业服务（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（Decision Making infrastructure，简称DecB）对大量战略代理人的影响。DMIs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。ASTrA进一步引入了一种系统的、分层的方法，从需求的表达中自动化DMI的设计、验证和确认。最后，它提供了一套尖端的计算工具，以促进我们的方法，使有效的推理之间的相互作用离散模型，例如，用于描述复杂任务或嵌入式软件组件，以及用于描述物理过程的连续模型。评估计划包括在为零净能量应用设计的真实的试验台上进行实验。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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
• 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

• DOI: 10.1109/lcsys.2022.3231556
• 发表时间: 2022-09
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: James Ferlez;Yasser Shoukry

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

• DOI: 10.1145/3632970
• 发表时间: 2022-04
• 期刊: ACM Transactions on Embedded Computing Systems
• 影响因子: 2
• 作者: Wael Fatnassi;Yasser Shoukry

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

• DOI: 10.1145/3576841.3585935
• 发表时间: 2023-02
• 期刊: Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)
• 作者: Mohanad Odema;James Ferlez;Goli Vaisi;Yasser Shoukry;M. A. Al Faruque

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
• 期刊: IEEE
• 作者: Odema, Mohanad;Ferlez, James;Shoukry, Yasser;Al Faruque, Mohammad Abdullah
• 通讯作者: Al Faruque, Mohammad Abdullah