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Title: A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems

Abstract

Cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital gridmore » is investigated as an illustrative example.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Univ. of Central Florida, Orlando, FL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1574239
Grant/Contract Number:  
EE0007327
Resource Type:
Accepted Manuscript
Journal Name:
Computational Intelligence and Optimization Methods for Control Engineering
Additional Journal Information:
Journal Name: Computational Intelligence and Optimization Methods for Control Engineering
Country of Publication:
United States
Language:
English

Citation Formats

Gusrialdi, Azwirman, Xu, Ying, Qu, Zhihua, and Simaan, Marwan A. A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems. United States: N. p., 2019. Web. doi:10.1007/978-3-030-25446-9_7.
Gusrialdi, Azwirman, Xu, Ying, Qu, Zhihua, & Simaan, Marwan A. A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems. United States. doi:10.1007/978-3-030-25446-9_7.
Gusrialdi, Azwirman, Xu, Ying, Qu, Zhihua, and Simaan, Marwan A. Fri . "A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems". United States. doi:10.1007/978-3-030-25446-9_7.
@article{osti_1574239,
title = {A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems},
author = {Gusrialdi, Azwirman and Xu, Ying and Qu, Zhihua and Simaan, Marwan A.},
abstractNote = {Cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital grid is investigated as an illustrative example.},
doi = {10.1007/978-3-030-25446-9_7},
journal = {Computational Intelligence and Optimization Methods for Control Engineering},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

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