Modeling, Identification, and Estimation of Distributed Parameter Systems Using Mobile Sensor Networks

使用移动传感器网络对分布式参数系统进行建模、识别和估计

• 批准号: 1663073
• 负责人: Wencen Wu
• 金额: $ 35.83万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663073&HistoricalAwards=false
• 关键词: Modeling; Identification; Estimation; Distributed; Parameter; Modeling; Identification; Estimation; Distributed; Parameter

This project will formulate a general purpose mathematical framework using mobile sensor networks (MSNs) that will allow an efficient and accurate prediction of information about behavior of complex real world systems such as weather forecasting, wildfire control, disaster recovery, explosive materials detection, etc. Such systems are known to be distributed parameter systems (DPS) and modeling of such systems and accurately predicting their behavior in real-time is highly complex and computationally challenging. The current state-of-the-art techniques use static sensor network to help obtain solutions from complex mathematical models which are often inaccurate and cannot provide timely information. The mobility and adaptability of MSNs make them great candidates for overcoming these challenges. This project will develop a unified framework for modeling, identifying, estimating and predicting behavior of such distributed parameter systems. The project will also develop a strongly integrated research, educational, and outreach program by providing graduate students with interdisciplinary and challenging research experiences, by providing undergraduate students with the opportunity of early involvement in research activities through algorithm development and test-bed experiments, and by motivating K-12 students by giving them hands-on experiences through university's Engineering Ambassadors(EA) program.The outcomes of this project are expected to advance the modeling, identification, and state estimation techniques for distributed parameter systems, offer comprehensive scientific understanding of the connections between DPS and mobile sensor networks, and contribute to generic engineering principles for designing cooperative control and distributed sensing strategies for mobile sensor networks. In particular, the work will: develop novel approaches for online parameter identification and state estimation of DPS using mobile sensor networks with reduced computational and communication cost compared to existing methods developed for static sensor networks; determine information-rich and energy-saving optimal trajectories of mobile sensor networks moving in DPS for simultaneously system identification and state prediction; and design a multi-robot test-bed with controllable advection-diffusion fields for the validation of the strategies, and conduct field experiments to test the strategies under realistic uncertainties and variations. The project also offers great educational opportunities for graduate, undergraduate, as well as K-12 students.

该项目将使用移动传感器网络（MSN）制定一个通用数学框架，该框架将允许对复杂现实世界系统的行为（例如天气预测，野火控制，灾难恢复，爆炸性材料检测等）进行有效，准确的预测。已知这些系统被公认为是分布的参数（DPS），并具有挑战性和准确的挑战和精确的行为。当前的最新技术使用静态传感器网络来帮助从通常不准确且无法提供及时信息的复杂数学模型中获取解决方案。 MSN的流动性和适应性使它们成为克服这些挑战的绝佳候选人。该项目将开发一个统一的框架，用于建模，识别，估计和预测此类分布式参数系统的行为。 The project will also develop a strongly integrated research, educational, and outreach program by providing graduate students with interdisciplinary and challenging research experiences, by providing undergraduate students with the opportunity of early involvement in research activities through algorithm development and test-bed experiments, and by motivating K-12 students by giving them hands-on experiences through university's Engineering Ambassadors(EA) program.The outcomes of this project are expected to advance the modeling,用于分布式参数系统的识别和状态估计技术，对DPS和移动传感器网络之间的联系提供了全面的科学理解，并为设计合作控制和移动传感器网络的分布式传感策略做出了一般工程原则。特别是，这项工作将：与针对静态传感器网络开发的现有方法相比，使用移动传感器网络使用移动传感器网络开发了在线参数识别和DPS的状态估计的新颖方法；确定信息丰富和节能的最佳移动传感器网络的最佳轨迹，以同时进行系统识别和状态预测；并设计一个具有可控的对流扩散字段的多机器人测试床，以验证策略，并进行现场实验，以测试现实的不确定性和变化下的策略。该项目还为研究生，本科生以及K-12学生提供了很大的教育机会。