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

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Computational Intelligence and Optimization Methods for Control Engineering

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 150))

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.

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Acknowledgements

This work is supported in part by U.S. Department of Transportation (award DTRT13GUTC51), by U.S. National Science Foundation (grant ECCS-1308928), by US Department of Energy (awards DE-EE0006340, DE-EE0007327, and DE-EE0007998), by L-3 Communication Coleman Aerospace (contract 11013I2034), by Texas Instruments’ awards, and by Leidos (contract P010161530).

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Authors and Affiliations

  1. Tampere University, Tampere, 33014, Finland

    Azwirman Gusrialdi

  2. University of Central Florida, Orlando, 32816, USA

    Ying Xu, Zhihua Qu & Marwan A. Simaan

Authors
  1. Azwirman Gusrialdi
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  2. Ying Xu
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  3. Zhihua Qu
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  4. Marwan A. Simaan
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Corresponding author

Correspondence to Azwirman Gusrialdi .

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Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA

    Maude Josée Blondin

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Institute of Automation and Industrial Computer, Polytechnic University of Valencia, Valencia, Spain

    Javier Sanchis Sáez

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Gusrialdi, A., Xu, Y., Qu, Z., Simaan, M.A. (2019). A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems. In: Blondin, M., Pardalos, P., Sanchis Sáez, J. (eds) Computational Intelligence and Optimization Methods for Control Engineering. Springer Optimization and Its Applications, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-25446-9_7

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