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: 2303157
• 负责人: Claus Danielson
• 金额: $ 31.09万
• 依托单位: University of New Mexico
• 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=2303157&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? Research will also incorporate diverse and inclusive STEM workforce development through mentoring and recruiting underrepresented groups and implementation of a multi-mentor model to enhance belonging. 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. Proposed research 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.

该赠款将支持开发新颖的计算工具和新知识，可直接从数据中安全地自动化复杂过程。尽管近年来，包括机器学习和AI在内的数据驱动方法已经提高了许多领域，但在控制复杂动力学系统（尤其是安全至关重要的系统）方面，它们的影响较不明显。这项赠款资助的研究将为安全和绩效提供严格的数据驱动保证，从而通过提高自动化系统的安全性来促进自治的科学并提高国家繁荣。但是，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，在那里我们只有有限的数据来表征任意复杂的非线性系统。这个新颖的范式对于没有第一原则模型或动态太贵或耗时的应用程序的自动化和控制的非传统应用具有吸引力，无法使用传统的系统识别来识别。特别是，该研究将应用于超声波的数据驱动自动化。自动化超声波将释放训练有素的医疗专业人员，从事其他患者护理，改善农村地区，欠发达国家和军事基础的医疗服务，在那里训练有素的技术人员稀缺，从而使美国经济和社会受益。该项目支持以这个问题为动机的研究：确保数据驱动范式中安全性和性能所需的数据数量和质量是多少？研究还将通过指导和招募代表性不足的群体以及实施多头模型来增强归属的多样化和包容性的STEM劳动力发展。该赠款支持的研究将解决基本问题，其答案将使数据驱动的正面，控制和承包不变的集合能够直接综合。这项研究的主要新颖性是开发了证明是不变的合成集的技术。这种方法的好处是由数据驱动的确保限制满意度的保证。这项研究具有潜在的变革性，因为它将直接从数据中直接对约束控制器进行分析和综合。同样，它将能够将基于名义模型的设计扩展到较大的操作领域，在这种域中，建模假设无效，同时提供了严格的，数据驱动的安全性，鲁棒性和性能。这种范式对于没有第一原则模型或动态过于昂贵或耗时而无法使用传统系统识别的不传统的控制的非传统应用具有吸引力。提出的研究是通过利用数据革命提供控制数据驱动控制的控制理论保证的动机。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来通过评估来支持的。