Adaptive Digital Twin Modeling with Control: Integration of Extended Kalman Filter-Based Recursive Sparse Nonlinear Identification with Model Predictive Control
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sensors-26-01734-v2.pdf
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11.41 MB
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328edba03f3df3028526b057b299a439
Author(s)
Wang, Jingyi
Cao, Liang
Cao, Yankai
Gopaluni, Bhushan
Date Issued
March 9, 2026
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Wang, Jingyi, Liang Cao, Yankai Cao, and Bhushan Gopaluni. 2026. "Adaptive Digital Twin Modeling with Control: Integration of Extended Kalman Filter-Based Recursive Sparse Nonlinear Identification with Model Predictive Control" Sensors 26, no. 5: 1734.
Version
Final published version
Abstract
The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes a comprehensive digital twin development framework that integrates digital twin identification, real-time model updating, and advanced process control. The proposed approach first identifies the offline digital twin model through the sparse identification of a nonlinear dynamics algorithm, reducing the digital twin development time while maintaining model fidelity. Then, the identified model is updated by the extended Kalman filter to mitigate the problem of diminishing accuracy. Finally, incorporating the latest updated model into the model predictive control facilitates the control inputs optimization and enhances the interactive capacity of digital twins. Through one industrial case study and two simulation examples, the advantages of the proposed algorithm are demonstrated.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s26051734
