CAREER: Machine Learning for Data-Driven Fault-Tolerant Control of Complex Systems

这个教师早期职业发展（Career）项目将创造关于复杂系统动态行为和控制的新知识；具体来说，如何预测复杂系统中罕见的有害事件，以及如何在发生故障时控制这些系统以达到预期的性能。复杂系统是由许多协作元素组成的网络，这些元素以非线性和反直觉的方式不断相互作用；例子包括网络安全、制造流程、自动化运输基础设施、医疗设备以及许多其他与我们的福祉相关的东西。这些系统中的故障是指故障，例如网络攻击或传感器故障，这些故障破坏了安全性，降低了系统功能，并导致安全问题和经济损失。控制这些系统是具有挑战性的，因为集成的动态行为本质上是难以预测的。该奖项支持建立“故障感知”控制框架的基础研究，以研究单个元素之间的相互作用如何产生集体动力，以及如何减轻故障对复杂系统的影响。为了开发和测试控制框架，将使用心室辅助装置管理的衰竭心脏作为基础，以(i)检测危及心力衰竭患者生存的设备故障，如血栓形成和抽吸；（ii）在故障情况下自动调整设备的操作，以提高患者的生活质量。教育和推广计划将侧重于促进积极和终身学习，并在各个层面吸引和培训学生，包括退伍军人向平民生活过渡，新兴行业和跨学科技能。利用机器学习作为主干，本研究的目标是创建一种数据驱动的控制策略，在故障发生后调节和维持系统的稳态，同时确保系统继续以无缝、连续的方式运行。本研究将填补在控制现象未知和第一原理模型难以获得的情况下复杂系统监督与控制的知识空白。数据驱动的策略也将克服设计上的限制。设计复杂的系统，如心室辅助装置，基于第一性原理模型是昂贵的，耗时的，并且需要广泛的专家知识来建立基于普遍存在的假设的特定应用模型，这些假设在实践中很难满足。本研究项目将数据分析、控制理论和机器学习整合到一个统一的框架中，具有三个创新方面：开发机器学习方法，直接从数据中发现故障的症状指纹，用于实时故障诊断；建立一个在线自适应建模范例，以预测由于经济考虑或技术限制而无法直接测量的与性能相关的变量；设计一个容错控制器，以提高系统的性能，同时确保满足所有操作约束。除了应用于心室辅助设备之外，该框架还可以应用于保护计算机系统免受数字攻击，提高制造效率，解决自动化运输基础设施和医疗设备中的安全问题，从而产生引人注目的社会和经济效益。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 Faculty Early Career Development (CAREER) project will create new knowledge about the dynamic behavior and control of complex systems; specifically, how to predict rare deleterious events in complex systems, and how to control these systems when faults occur to achieve a desired performance. Complex systems are networks comprising many collaborating elements that continuously interact with each other in a nonlinear and counterintuitive manner; examples include cybersecurity, manufacturing processes, automated transportation infrastructure, medical devices, and many others relevant to our well-being. Faults in these systems are malfunctions, such as cyber-attack or sensor failure, that break security, degrade system functionality, and cause safety concerns and economic losses. Control of these systems is challenging because the dynamic behavior of the ensemble is intrinsically difficult to predict. This award supports fundamental research to build a “fault-aware” control framework to study how interactions among individual elements produce the collective’s dynamics and how to alleviate the effect of faults on complex systems. To develop and test the control framework, a failing heart managed by a ventricular assist device will be used as the foundation to (i) detect device faults such as thrombosis and suction that jeopardize the survival of heart failure patients and (ii) automatically adjust the operation of the device under faults to improve the patient quality of life. The educational and outreach plan will focus on promoting active and life-long learning and engaging and training students at various levels, including veterans transitioning to civilian life, in emerging industries and transdisciplinary skills.Using machine learning as the backbone, the objective of this research is to create a data-driven control strategy that regulates and maintains the system’s homeostasis following the onset of faults, while ensuring the system continues to operate in a seamless, continuous manner. This research will fill the knowledge gap for the supervision and control of complex systems when the governing phenomena are unknown and when first principle models are not readily attainable. The data-driven strategy will also overcome design limitations. Designing complex systems, such as ventricular assist devices, based on first principle models is costly, time consuming, and requires extensive expert knowledge to build application-specific models based on ubiquitous assumptions that are difficult to satisfy in practice. This research project will integrate data analytics, control theory, and machine learning into a unified framework with three innovative aspects: developing machine learning methods to discover symptomatic fingerprints of faults directly from data for real-time fault diagnosis; building an online adaptive modeling paradigm to predict performance-related variables that are not directly measurable due to economic considerations or technical constraints; designing a fault-tolerant controller to improve the system’s performance, while ensuring all operational constraints are met. In addition to its application to ventricular assist devices, this framework can be applied to protect computer systems from digital attacks, improve manufacturing efficiency, and address safety issues in automated transportation infrastructure and medical devices, leading to compelling societal and economic benefits.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.