Big Data-driven Predictive Control Using Multi-view Clustering

This work presents a big data-driven predictive control (BDPC) approach using multi-view clustering to approximate nonlinear system behaviors (represented by a set of input-output variable trajectories) with local linear sub-behaviors (represented by Hankel matrices). The nonlinear behavior space is...

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Bibliographic Details
Published inProceedings of the American Control Conference pp. 5100 - 5105
Main Authors Han, Shuangyu, Yan, Yitao, Bao, Jie, Huang, Biao
Format Conference Proceeding
LanguageEnglish
Published AACC 10.07.2024
Subjects
Aerospace electronics
Euclidean distance
Nonlinear systems
Optimization
Predictive control
Trajectory
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Summary:This work presents a big data-driven predictive control (BDPC) approach using multi-view clustering to approximate nonlinear system behaviors (represented by a set of input-output variable trajectories) with local linear sub-behaviors (represented by Hankel matrices). The nonlinear behavior space is partitioned based on two views: Euclidean distance of trajectories, and the angle of linear subspaces that trajectories belong to. Subsequently, a BDPC controller is designed to locate the online trajectory into the most relevant linear sub-behavior and determine control actions subject to optimization in every receding horizon. Finally, the BDPC approach is illustrated using an example of controlling the Hall-Héroult process.
ISSN:2378-5861
DOI:10.23919/ACC60939.2024.10645047