This paper presents two new adaptive model predictive control algorithms, both consisting of
an on-line process identification part and a predictive control part. Both parts are executed at …

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical
processes via artificial neural networks (ANN) by using corrupted data (ie, noisy data) is an …

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …

In this article, we present the application of a neural-network-based model predictive control
scheme to control pH in a laboratory-scale neutralization reactor. We use a feedforward …

This paper discusses an industrial application of a multivariable nonlinear
feedforward/feedback model predictive control where the model is given by a dynamic …

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 …

The use of neural networks (NNs) in all aspects of process engineering activities, such as
modelling, design, optimization and control has considerably increased in recent years …

A constrained multivariable control strategy along with its application in more efficient
thermal power plant control is presented in this paper. A neural network model-based …

Model predictive control of nonlinear sampled-data systems is studied, with a particular
focus on computational efficiency. In order to reduce the computational requirements …

A nonlinear internal model control (NIMC) strategy based on neural network models is
proposed for SISO processes. The neural network model is identified from input—output …