An Adaptive System Identification Approach Using Mobile Sensors

该项目将通过开展研究，提供一种变革性的方法，使识别具有少量传感器的大型动态系统，从而有利于国家利益。研究结果对桥梁、管道、建筑物、涡轮机塔架等结构的损伤诊断和性能评估具有一定的参考价值。特别是，传感器放置的迭代方法将为系统识别和损伤定位提供前所未有的能力，输入决策支持框架，以指导有效的结构修复。这种能力有可能加快目前民用基础设施的检查过程，并每年减少数亿美元的成本。该项目的一个组成部分是在系统识别，反馈控制，机器人和结构工程领域的本科生和研究生的启发和教育。该项目还将通过塔夫茨大学的学生教师外展导师计划接触K-12学生。来自少数民族的学生的参与是一个优先事项，并将通过直接与塔夫茨大学的既定计划合作来促进。通过这个项目，将开发一种新的系统识别框架，其中识别是使用顺序数据处理实现的，并且其中移动的传感器被反复重新部署，以提高识别系统模型的准确性。对于具有移动的传感器的系统识别，将采用基于贝叶斯推理的识别和强化学习技术来基于从当前传感器布置获得的反馈来找到传感器的未来位置。该项目的方法还将包括路径规划部分和数据融合部分。路径规划组件平衡建模精度与能量和时间约束，以确定在每个迭代步骤中用于数据采集的最佳候选位置，而数据融合组件将确定如何尽可能有效地将联合收割机先前模型与新数据相结合，同时考虑移动的传感器处的通信和计算的资源约束。该项目的方法的能力最终将在一个案例研究中进行评估，用于设想的结构诊断应用。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

This project will benefit national interests by conducting research that provides a transformative approach to enable the identification of large dynamical systems with a small number of sensors. The results will be useful for applications like the damage diagnosis and performance assessment of bridges, pipelines, buildings, wind turbine towers, and other structures. In particular, the iterative approach to sensor placement will provide an unprecedented capability for system identification and localization of damage, feeding into a decision support framework to guide effective structural repair. This capability has the potential to speed current inspection processes for civil infrastructure and reduce costs by hundreds of millions of dollars annually. An integral part of this project is the inspiration and education of undergraduate and graduate students in the areas of system identification, feedback control, robotics, and structural engineering. This project will also reach K-12 students through Tufts' Student Teacher Outreach Mentorship Program. Participation of students from minorities is a priority and will be promoted by working directly with established programs at Tufts University.Through this project a novel system identification framework will be developed, where identification is implemented using sequential data processing, and where mobile sensors are redeployed iteratively to enhance the accuracy of the identified system model. For system identification with mobile sensors, Bayesian inference-based identification and reinforcement learning techniques will be employed to find the future locations of sensors based on the feedback obtained from the current sensor placement. The methodology in this project will also include a path-planning component and a data-fusion component. The path-planning component balances modeling accuracy with energy and time constraints to determine the best candidate locations for data acquisition at each iterative step, while the data-fusion component will determine how to combine prior models with new data as efficiently as possible, considering resource constraints on communication and computation at the mobile sensors. The capabilities of the project's approach will finally be evaluated on a case study for the envisioned structural diagnosis application.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.