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

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Details

Original languageEnglish
Title of host publicationComputational Intelligence and Optimization Methods for Control Engineering
PublisherSpringer International Publishing
Pages149-172
Number of pages24
ISBN (Electronic)978-3-030-25446-9
ISBN (Print)978-3-030-25445-2
DOIs
Publication statusPublished - 2019
Publication typeA3 Part of a book or another research book

Publication series

NameSpringer Optimization and Its Applications
Volume150
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

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 power grid is investigated as an illustrative example.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland