Data predictive control of nonlinear process feature dynamics through latent variable behaviours

The paper presents a new big data-based approach to control of feature dynamics of continuous nonlinear chemical/industrial processes, based on the behavioural systems theory and deep learning tools. From time-series process data, the feature dynamics of a nonlinear process are extracted using an Autoencoder (AE), a type of artificial neural network. The feature dynamics are embedded in, and can be constructed from, a linear dynamic behaviour of latent variables. The latent variable dynamic space is described by a kernel representation and linearly maps the feature variable space. A Data Predictive Control (DPC) approach is developed to optimise the feature variables by controlling the latent variable dynamics using a system behaviour framework. Behaviour-based dissipativity conditions are adopted to deal with errors that arise in the latent and feature variable spaces during neural network training. A case study is presented to illustrate the proposed approach. (C) 2022ElsevierLtd. Allrightsreserved.

Automated summary

This paper presents a new big data-based approach to control the feature dynamics of continuous nonlinear chemical/industrial processes, using the behavioural systems theory and deep learning tools. The feature dynamics are extracted using an Autoencoder, and the optimization of feature variables is achieved by controlling the latent variable dynamics.