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.

这笔赠款将支持开发新的计算工具和新知识，这些工具和新知识可用于直接从数据安全地自动化复杂的过程。尽管包括机器学习和人工智能在内的数据驱动方法近年来在许多领域取得了进展，但它们在复杂动力系统控制方面的影响却不那么明显，尤其是对安全至关重要的系统。这项由这笔拨款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进步自主科学并促进国家繁荣。然而，这需要新的知识和计算工具来克服数据驱动范例的固有不确定性，在这种范例中，我们只有有限的数据来表征任意复杂的非线性系统。这种新的范例对于没有第一原理模型的自动化和控制的非传统应用很有吸引力，或者对于那些动态太昂贵或太耗时而无法使用传统系统辨识的应用。特别是，这项研究将应用于数据驱动的超声波自动化。自动化超声波将解放训练有素的医疗专业人员，让他们从事其他领域的病人护理，改善农村地区、欠发达国家和军事基地的医疗保健，这些地区缺乏训练有素的技术人员，从而使美国经济和社会受益。该项目支持以以下问题为动机的研究：在数据驱动的范例中，保证安全性和性能所需的数据数量和质量是多少？研究还将纳入多样化和包容性的STEM劳动力发展，方法是指导和招聘代表性不足的群体，并实施多导师模式以增强归属感。由这笔拨款支持的研究将解决基本问题，这些问题的答案将使正、控制和压缩不变量集的直接数据驱动综合成为可能。这项研究的主要新颖性是开发了合成可证明不变的集合的技术。这种方法的好处是数据驱动的约束满足保证。这项研究具有潜在的变革性，因为它将允许直接根据数据分析和综合约束强制控制器。同样，它将使标称的基于模型的设计扩展到建模假设无效的更大的操作领域，同时提供严格的、数据驱动的安全、健壮性和性能保证。对于没有第一原理模型的非传统控制应用，或者其动态太昂贵或太耗时而无法使用传统系统辨识的应用，这种范例很有吸引力。拟议的研究旨在利用数据革命为数据驱动的控制提供控制理论保证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。