Big Data-Driven Control of Nonlinear Processes Through Dynamic Latent Variables Using an Autoencoder

This article 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 knowledge of 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 variables under dissipativity conditions.