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Big Data-driven Control of Nonlinear Processes through Dynamic Latent Variables using an Autoencoder
  • +1
  • Jun Wen Tang,
  • Yitao Yan,
  • Jie Bao,
  • Biao Huang
Jun Wen Tang

Corresponding Author:[email protected]

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Yitao Yan
Jie Bao
Biao Huang

Abstract

This paper presents a novel data-driven approach to nonlinear system control using a behavioral systems framework. A Dynamic Latent Variable Autoencoder (DLVAE) is proposed to project the nonlinear physical variable space onto a linear latent variable space. A data-predictive control approach is developed to control the physical process variables through the latent variables. Based on the behavioral systems theory, the proposed data-driven control framework does not require the knowledge on the causality of the latent variables. The stability of the controlled system is ensured by utilizing the concept of trajectory-based dissipativity. The robustness of this control approach is achieved by incorporating the Lipschitz bounds between the latent and physical variable under dissipativity conditions.
23 May 2024Submitted to TechRxiv
30 May 2024Published in TechRxiv