The design of neural networks (NNs) is presented for treating large, linear model predictive
control (MPC) applications that are out of reach with available quadratic programming (QP) …

An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …

Scenario-based model predictive control (MPC) methods introduce recourse into optimal
control and can thus reduce the conservativeness inherent to open-loop robust MPC …

This work provides experimental demonstration for a previously proposed parallel model
structure for general nonlinear model-predictive control (NMPC). The model comprises of a …

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 …

A neural-network-based technique for developing nonlinear dynamic models from empirical
data for an model predictive control (MPC) algorithm is presented. These models can be …

This work addresses one aspect of the overparameterization problem in using
artificial/recurrent neural networks (ANN/RNN) based dynamic models for model predictive …

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 …

Abstract Neural Ordinary Differential Equation (NODE) is a recently proposed family of deep
learning models that can perform a continuous approximation of a linear/nonlinear dynamic …

With the widespread availability of model predictive control (MPC), nonlinear MPC provides
a natural extension to include nonlinear models for trajectory tracking and dynamic …