Fast Situational Awareness and Reliable Response with Heterogeneous Feedback and Number-Theoretic Control Primitives

这项拨款将资助研究，使工程系统能够有效地依赖和响应从多个不同和异步来源收集的实时数据，并应用于医疗设备技术和航空航天制造平台，从而促进科学进步，促进国家繁荣。快速和统一的计时是当前构建实时系统的准则。然而，这样的系统越来越多地在无处不在的、各种形式的数据和异步的、通常是不规则的工作流中运行。关于如何在设计和控制下一代关键系统（包括那些旨在帮助人类处理数据密集型情况的系统）的行为中利用这些数据流的基础知识是缺乏的。该项目将通过建立一个新的理论和算法框架来克服这一知识差距，该框架展示了如何将异构的、通常缓慢的测量可靠而快速地结合起来，以实现快速的信息检索和强大的反馈响应。该框架将使超越现有流程监控边界的无与伦比的态势感知和响应敏捷性成为可能。一套相关的互动演示和动态可视化将通过网络应用程序开发和传播，以培养所有年龄段学生的算法思维心态。将研究成果整合到高级技术课程中，并让本科生和研究生参与研究，将产生额外的教育影响。本研究旨在为如何捕获和组合异步采样传感器数据的理论做出基础贡献，以最大化实时信息收集，增加过程可观察性，并为闭环自治设计快速鲁棒的反馈。它通过首先开发一种用于传感器管理的数论协作采样方法来实现这一结果，该方法适用于一般动态系统和具有数学可表达的样本内相关性的广泛过程信号。其次，导出了在不确定性条件下保持可观测性和实现概率最优抽样的条件。最后，基于youla - kuucera参数化的外环控制器被定制，以实现超出单个传感器奈奎斯特频率的高性能跟踪。数论参数化和反馈控制算法将在粉末床融合增材制造和复杂金属物体自动机器人检测中的激光-材料相互作用实验中进行评估。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 fund research that enables engineered systems to effectively rely on and respond to real-time data collected from multiple diverse and asynchronous sources, with applications to medical device technology and aerospace manufacturing platforms, thereby promoting the progress of science, and advancing the national prosperity. Rapid and uniform timekeeping is the current dogma for building real-time systems. Such systems, however, increasingly operate in the presence of ubiquitous and diverse forms of data and asynchronous, often irregular workflows. Foundational knowledge about how to leverage such data streams in designing and controlling the behavior of the next generation of critical systems, including those meant to assist humans in data-intensive situations, is lacking. This project will overcome this knowledge gap by building a new theoretical and algorithmic framework that demonstrates how heterogeneous, often slow measurements can be reliably and quickly combined to achieve fast information retrieval and a robust feedback response. This framework will enable unsurpassed situational awareness and response agility beyond existing boundaries of process monitoring. A suite of related interactive demonstrations and dynamic visualizations will be developed and disseminated through web applications to foster a mindset of algorithmic thinking in students of all ages. Additional educational impact will result from integration of research results in advanced technical coursework and engagement of undergraduate and graduate students in research.This research aims to make fundamental contributions to a theory of how asynchronously sampled sensor data may be captured and combined to maximize real-time information gathering, increase process observability, and design rapid and robust feedback for closed-loop autonomy. It achieves this outcome by first developing a number-theoretic collaborative sampling approach for sensor management that applies to general dynamic systems and a broad range of process signals with mathematically expressible intra-sample correlations. Next, it derives conditions on the sampling that preserve observability and enable probabilistic optimal sampling subject to uncertainty. Finally, an outer loop controller based on the Youla-Kucera parameterization is tailored to achieve high-performance tracking beyond the Nyquist frequency of the individual sensors. The number-theoretic parameterizations and feedback control algorithms will be evaluated in experiments on laser-material interactions in powder bed fusion additive manufacturing and automated robotic inspection of complex metallic objects.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.