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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

基本信息

批准号：
1917300
负责人：
Wencen Wu
金额：
$ 29.82万
依托单位：
San Jose State University Foundation
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 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917300&HistoricalAwards=false
关键词：
Modeling Identification Estimation Distributed Parameter

项目摘要

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.

该项目将使用移动的传感器网络（MSN）制定一个通用的数学框架，该框架将允许有效准确地预测有关复杂真实的世界系统行为的信息，如天气预报，野火控制，灾难恢复，爆炸物检测，已知这样的系统是分布参数系统（DPS），并且对这样的系统进行建模并精确地预测它们在真实的中的行为。时间是高度复杂的并且在计算上具有挑战性。当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，这些模型通常不准确并且不能提供及时的信息。MSN的移动性和适应性使其成为克服这些挑战的绝佳候选者。本计画将发展一个统一的架构，以建立模型、辨识、估计与预测这类分布参数系统的行为。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，通过为本科生提供通过算法开发和试验台实验早期参与研究活动的机会，并通过大学的工程大使（EA）计划为K-12学生提供实践经验来激励他们。该项目的成果预计将推动建模，识别和分布参数系统的状态估计技术，提供了DPS和移动的传感器网络之间的连接的全面的科学理解，并有助于设计合作控制和移动的传感器网络的分布式传感策略的一般工程原理。特别是，这项工作将：开发新的方法在线参数识别和状态估计的DPS使用移动的传感器网络与减少计算和通信成本相比，现有的方法开发的静态传感器网络;确定信息丰富和节能的最佳轨迹的移动的传感器网络移动在DPS的同时系统识别和状态预测;设计具有可控平流扩散场的多机器人测试台来验证策略，并进行现场实验来测试现实不确定性和变化情况下的策略。该项目还为研究生，本科生以及K-12学生提供了很好的教育机会。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Design and Implementation of a Small-scale Autonomous Vehicle for Autonomous Parking

DOI：
10.1109/cacre52464.2021.9501311
发表时间：
2021-07
期刊：
2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)
影响因子：
0
作者：
Tianhao Yu;W. Lu;Yanshen Luo;Chenguang Niu;Wencen Wu
通讯作者：
Tianhao Yu;W. Lu;Yanshen Luo;Chenguang Niu;Wencen Wu

Cooperative Level Curve Tracking in Advection-Diffusion Fields

DOI：
10.1109/cacre50138.2020.9230059
发表时间：
2020-09
期刊：
2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE)
影响因子：
0
作者：
Sachin Guruswamy;Wencen Wu
通讯作者：
Sachin Guruswamy;Wencen Wu

LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots