This work explores the design of distributed model predictive control (DMPC) systems for
nonlinear processes using machine learning models to predict nonlinear dynamic behavior …

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 …

In this work, we develop reduced-order machine learning models using feature selection
methods for distributed model predictive control (DMPC) of nonlinear processes …

We present a machine learning-based predictive control scheme that integrates an online
update of the recurrent neural network (RNN) models to capture process nonlinear …

In this work, we focus on distributed model predictive control of large scale nonlinear
process systems in which several distinct sets of manipulated inputs are used to regulate the …

This work develops a model predictive control (MPC) scheme using online learning of
recurrent neural network (RNN) models for nonlinear systems switched between multiple …

In this work, we focus on distributed model predictive control (DMPC) of large scale
nonlinear process systems in which several distinct sets of manipulated inputs are used to …

In this work the radial basis function neural network architecture is used to model the
dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which …

In order to approximate nonlinear dynamic systems utilizing time-series data, recurrent
neural networks (RNNs) and long short-term memory (LSTM) networks have frequently been …

Abstract Machine learning is receiving more attention in classical engineering fields, and in
particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have …