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

职业：用于复杂系统数据驱动容错控制的机器学习

• 批准号: 2426614
• 负责人: Yuncheng Du
• 金额: $ 59.43万
• 依托单位: University of Houston
• 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: 2023-11-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2426614&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Data; Driven; CAREER; Machine; Learning; Data; Driven

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.

这个教师的早期职业发展（职业）项目将创建有关复杂系统的动态行为和控制的新知识；具体而言，如何预测复杂系统中罕见的有害事件，以及在发生故障以实现所需性能时如何控制这些系统。复杂的系统是网络完成许多协作元素，这些元素以非线性和违反直觉的方式连续相互作用。例子包括网络安全，制造过程，自动运输基础设施，医疗设备以及许多与我们的福祉相关的其他人。这些系统中的故障是故障，例如网络攻击或传感器故障，破坏了安全性，降低系统功能以及引起安全问题和经济损失。对这些系统的控制是具有挑战性的，因为整体的动态行为本质上很难预测。该奖项支持基础研究，以建立“缺陷感”控制框架，以研究各个元素之间的相互作用如何产生集体的动态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，通过心室辅助设备管理的失败心脏将被用作（i）检测器件故障（例如血栓形成和吸力），危害心力衰竭患者的存活，并自动调整设备在缺陷下的设备运行以提高生活质量。教育和宣传计划将专注于促进各个层次的积极和终身学习，参与和培训学生，包括退伍军人过渡到平民生活，在新兴行业和跨学科的技能中。将机器学习作为骨干的目标，这项研究的目标是创建一个由数据驱动的控制策略，从而在不断地进行系统的稳定效果，同时在不断的系统中进行操作，从而使系统不断地进行操作。当执政现象未知时，并且在不容易达到第一个原则模型时，这项研究将填补对复杂系统的监督和控制的知识差距。数据驱动的策略还将克服设计限制。基于第一个原则模型，设计复杂的系统（例如心室辅助设备）是昂贵的，耗时，并且需要广泛的专家知识来建立基于无处不在的假设，这些模型在实践中难以满足。该研究项目将将数据分析，控制理论和机器学习整合到具有三个创新方面的统一框架中：开发机器学习方法，以直接从数据中直接从数据中发现故障的症状指纹进行实时故障诊断；构建在线自适应建模范式，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；设计容忍故障的控制器以提高系统的性能，同时确保满足所有操作限制。除了将其应用于心室辅助设备外，还可以应用该框架来保护计算机系统免受数字攻击，提高制造效率，并解决自动运输基础设施和医疗设备的安全问题，从而导致令人信服的社会和经济益处。该奖项反映了NSF的法定任务，并通过评估基金会的智力和广泛的影响来诚实地对其进行评估。