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

这笔拨款将支持新型计算工具和新知识的开发，这些工具和新知识可用于直接从数据中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，近年来在许多领域取得了进展，但它们在复杂动态系统的控制方面的影响并不明显，尤其是对安全至关重要的系统。由这笔拨款资助的研究将为安全性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表征任意复杂的非线性系统。这种新颖的范例对于没有第一性原理模型的自动化和控制的非传统应用或使用传统系统识别识别动力学过于昂贵或耗时的应用具有吸引力。特别是，该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，改善缺乏训练有素的技术人员的农村地区、不发达国家和军事基地的医疗保健，从而使美国经济和社会受益。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，保证安全性和性能所需的数据的数量和质量是多少？研究还将通过指导和招募代表性不足的群体，以及实施多导师模式来加强归属感，纳入多样化和包容性的STEM劳动力发展。该基金支持的研究将解决一些基本问题，这些问题的答案将使正、控制和收缩不变量集的直接数据驱动综合成为可能。本研究的主要新颖之处在于开发了合成可证明不变量集的技术。这种方法的好处是数据驱动的约束满足保证。这项研究具有潜在的变革性，因为它将允许直接从数据中分析和综合约束强制控制器。同样，它将使名义上基于模型的设计扩展到更大的操作领域，其中建模假设无效，同时提供严格的、数据驱动的安全性、健壮性和性能保证。这种范式对于没有第一性原理模型的非传统控制应用或使用传统系统识别识别动力学过于昂贵或耗时的应用具有吸引力。提出的研究的动机是利用数据革命为数据驱动控制提供控制理论保证。该奖项反映了美国国家科学基金会的法定使命，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。

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.