Integrating autoencoder with Koopman operator to design a linear data‐driven model predictive controller

Abstract Non‐linear model predictive control (NMPC) is increasingly seen as a promising tool to tackle the problem of handling process nonlinearity and achieve optimal operation. One roadblock to NMPC implementation, however, is the lack of a good model, whether a first‐principles‐based or a non‐linear data‐driven‐based model such as artificial neural networks (ANN). This manuscript proposes a data‐driven modelling approach that integrates an …