Machine learning‐based predictive control of nonlinear processes. Part I: Theory

TLDR

Model predictive control for nonlinear processes is advanced by using ensembles of recurrent neural network models to forecast system dynamics. The paper aims to design model predictive control systems for nonlinear processes that employ RNN ensembles for dynamic prediction. The authors develop Lyapunov‑based MPC using RNN ensembles trained on open‑loop simulation data, enhance prediction accuracy with ensemble regression, and accelerate computation via parallel processing.

Abstract

Abstract This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on a data set generated from extensive open‐loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov‐based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed‐loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed‐loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.