CAREER: Multi-Objective Optimization of Sensor Placement for Reliable Monitoring and Control of Structures

职业：多目标优化传感器放置以实现结构的可靠监测和控制

• 批准号: 1750225
• 负责人: Lauren Linderman
• 金额: $ 50万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750225&HistoricalAwards=false
• 关键词: CAREER; Multi; Objective; Optimization; Sensor

This Faculty Early Career Development Program (CAREER) grant aims to sustain the long-term performance of civil infrastructure by identifying the most effective measurement types and locations for monitoring and isolating structural response. Continued performance of civil structures in daily use and after a natural hazard event is critical to the resilience of our communities and the US economy. The integration of sensor networks and physical systems is essential for maintaining this continued performance of civil structures while facing challenges in aging, energy, and the environment. The interaction of these cyber-physical components impacts almost every endeavor today and is indispensable in tackling today's broader challenges and continued US economic growth. This research project will identify optimal network systems for infrastructure applications through the integration of virtual and physical sensors and actuators. Unlike current approaches that are limited to specific measurement technology, function, and/or application, the new network approach is scalable to monitor large structures, as well as to provide insight on performance limits and maximize the reliability of the selected network configuration. Experimental modules will be developed based on the framework of this research through student's design projects and will be used to create an interactive outreach platform for students in secondary school.Effective implementation of sensor and actuator networks necessitates that limited sensor resources provide the most informative measured data and are reliable in the face of uncertainties. The objective of this research is a framework to identify the bounds of algorithm performance for structural health monitoring and control applications through optimization of sensor placement, type, and configuration in the presence of uncertainties. The multi-objective optimization sensor approach will be validated with results from physical tests ranging from laboratory-scale monitoring and control experiments through in-situ deployments. Key results include: (i) a sparsity-promoting algorithm to determine near-optimal sensor requirements for effective response and parameter estimation, (ii) insight into the performance bounds for estimation-based algorithms, and (iii) a novel experimental approach for validation of the technique under repeated, non-idealized conditions to establish the reliability of estimation-based approaches.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.

这项教师早期职业发展计划（Career）资助旨在通过确定最有效的测量类型和位置来监测和隔离结构响应，从而维持民用基础设施的长期性能。民用建筑在日常使用和自然灾害事件发生后的持续性能对我们社区和美国经济的恢复能力至关重要。在面临老化、能源和环境挑战的同时，传感器网络和物理系统的集成对于保持民用结构的持续性能至关重要。这些网络物理组件的相互作用几乎影响着当今的每一项努力，对于应对当今更广泛的挑战和持续的美国经济增长是不可或缺的。该研究项目将通过虚拟和物理传感器和执行器的集成来确定基础设施应用的最佳网络系统。与目前仅限于特定测量技术、功能和/或应用的方法不同，新的网络方法可扩展到监测大型结构，并提供对性能限制的洞察，并最大限度地提高所选网络配置的可靠性。实验模块将基于本研究的框架，通过学生的设计项目来开发，并将用于为中学生创建一个互动的推广平台。传感器和执行器网络的有效实施需要有限的传感器资源提供最具信息量的测量数据，并且在面对不确定性时是可靠的。本研究的目的是建立一个框架，通过在存在不确定性的情况下优化传感器的放置、类型和配置，来确定结构健康监测和控制应用的算法性能界限。多目标优化传感器方法将通过从实验室规模的监测和控制实验到现场部署的物理测试结果进行验证。关键结果包括：(i)一种促进稀疏性的算法，用于确定有效响应和参数估计的近最佳传感器要求，（ii）对基于估计的算法的性能界限的洞察，以及（iii）一种新的实验方法，用于在重复的非理想条件下验证该技术，以建立基于估计的方法的可靠性。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。

项目成果

Analysis of Networked Structural Control With Packet Loss

丢包的网络结构控制分析

• DOI: 10.1109/tcst.2021.3051578
• 发表时间: 2022
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Truong, Thao H.;Seiler, Peter;Linderman, Lauren E.
• 通讯作者: Linderman, Lauren E.