Collaborative Research: Data-Driven Invariant Sets for Provably Safe Autonomy

协作研究：数据驱动的不变集可证明安全的自治

• 批准号: 2303158
• 负责人: Leila Bridgeman
• 金额: $ 36.09万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303158&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Invariant

This grant will support the development of novel computational tools and new knowledge that can be used to safely automate complex processes directly from data. While data-driven methods, including machine learning and AI, have advanced numerous fields in recent years, their impact has been less pronounced in the control of complex dynamical systems, especially safety-critical ones. The research funded by this grant will provide rigorous data-driven guarantees on safety and performance, progressing the science of autonomy and advancing national prosperity by increasing the safety of automated systems. However, this requires new knowledge and computational tools to overcome the inherent uncertainty of a data-driven paradigm, where we only have finite data to characterize an arbitrarily complicated, nonlinear system. This novel paradigm is attractive for non-traditional applications of automation and control without first-principle models or applications whose dynamics are too expensive or time-consuming to identify using traditional system identification. In particular, the research will be applied to data-driven automation of ultrasounds. Automating ultrasounds will free up highly trained medical professionals to engage in other areas of patient care, improving medical care in rural areas, underdeveloped nations, and military-bases, where highly trained technicians are scarce, benefiting the U.S. economy and society. This project supports research that is motivated by the question: What is the quantity and quality of data required to guarantee safety and performance in a data-driven paradigm? The research supported by this grant will address fundamental questions whose answers will enable direct data-driven synthesis of positive, control, and contractive invariant sets. The primary novelty of this research is the development of techniques for synthesizing sets that are provably invariant. The benefit of this approach is data-driven guarantees of constraint satisfaction. This research is potentially transformative since it will allow the analysis and synthesis of constraint enforcing controller directly from data. Likewise, it will enable the extension of nominal model-based designs to larger operating domains where the modeling assumptions are invalid while providing rigorous, data-driven assurances of safety, robustness, and performance. This paradigm is attractive for non-traditional applications of control without first-principle models or applications whose dynamics are too expensive or time-consuming to identify using traditional system identification. Our approach is motivated by harnessing the data revolution to provide control theoretic guarantees for data-driven control.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.

这笔赠款将支持开发新的计算工具和新知识，这些工具和知识可用于直接从数据安全地自动化复杂过程。虽然数据驱动的方法，包括机器学习和人工智能，近年来已经推进了许多领域，但它们在控制复杂动态系统，特别是安全关键系统方面的影响并不明显。由该基金资助的研究将为安全和性能提供严格的数据驱动保证，通过提高自动化系统的安全性来推进自主科学和促进国家繁荣。然而，这需要新的知识和计算工具来克服数据驱动范式的固有不确定性，在这种范式中，我们只有有限的数据来描述任意复杂的非线性系统。这种新的范例是有吸引力的自动化和控制的非传统的应用程序，没有第一原理模型或应用程序的动态是太昂贵或耗时，以确定使用传统的系统识别。特别是，该研究将应用于数据驱动的超声波自动化。超声自动化将使训练有素的医疗专业人员能够从事其他患者护理领域，改善农村地区，欠发达国家和军事基地的医疗保健，这些地区训练有素的技术人员稀缺，有利于美国经济和社会。该项目支持的研究是由以下问题驱动的：在数据驱动的范式中，保证安全和性能所需的数据的数量和质量是什么？这项资助支持的研究将解决基本问题，其答案将使直接的数据驱动合成的积极，控制和收缩不变集。这项研究的主要新奇是开发了合成可证明不变集的技术。这种方法的好处是数据驱动的约束满足保证。这项研究是潜在的变革，因为它将允许直接从数据的约束执行控制器的分析和合成。同样，它将使基于标称模型的设计扩展到建模假设无效的更大操作域，同时提供严格的数据驱动的安全性，鲁棒性和性能保证。这种模式是有吸引力的非传统应用程序的控制没有第一原理模型或应用程序的动态是太昂贵或耗时，以确定使用传统的系统识别。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。