An Adaptive System Identification Approach Using Mobile Sensors

Tufts University, Medford MA

Investigators

Abstract

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.