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

这个项目将使用移动传感器网络(MSN)来制定一个通用的数学框架，该框架将允许高效和准确地预测复杂现实世界系统的行为信息，如天气预报、野火控制、灾难恢复、爆炸材料检测等。此类系统是已知的分布参数系统(DPS)，此类系统的建模和实时准确预测它们的行为是高度复杂和计算上具有挑战性的。目前最先进的技术使用静态传感器网络来帮助从复杂的数学模型中获得解决方案，这些数学模型往往不准确，不能提供及时的信息。MSN的移动性和适应性使它们成为克服这些挑战的极佳候选者。该项目将开发一个统一的框架，用于对这种分布参数系统的行为进行建模、识别、估计和预测。该项目还将通过为研究生提供跨学科和具有挑战性的研究经验，通过通过算法开发和试验台实验为本科生提供早期参与研究活动的机会，通过通过大学的工程大使(EA)计划通过给予K-12学生实践经验来激励他们，开发一个高度集成的研究、教育和推广计划。该项目的成果有望促进分布式参数系统的建模、识别和状态估计技术，为DPS和移动传感器网络之间的连接提供全面的科学理解，并有助于为移动传感器网络设计协作控制和分布式传感策略的通用工程原理。具体工作包括：开发基于移动传感器网络的分布式电源系统在线参数辨识和状态估计的新方法，与已有的静态传感器网络在线参数辨识和状态估计方法相比，具有较低的计算和通信成本；确定信息丰富且节能的移动传感器网络在分布式电源系统中运行的最优轨迹，以同时进行系统辨识和状态预测；设计具有可控平流扩散场的多机器人试验台以验证策略的有效性，并在现实的不确定性和变化情况下进行现场实验。该项目还为研究生、本科生和K-12学生提供了很好的教育机会。

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.