Neural networks hold great promise for application in the general area of process control.
This paper focuses on using a back propagation network in an optimization based model …

A neural network controller is applied to the optimal model predictive control of constrained
nonlinear systems. The control law is represented by a neural network function …

Neural network model predictive controllers have demonstrated high potential in the non-
conventional branch of nonlinear control. However, the major issue in process control of …

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 …

Design and implementation are studied for a neural network-based predictive controller
meant to govern the dynamics of non-linear processes. The advantages of using neural …

A neural network based predictive controller design algorithm is introduced for non-linear
control systems. It is shown that the use of non-linear programming techniques can be …

An efficient implementation of Generalized Predictive Control using a multi-layer
feedforward neural network as the plant's nonlinear model is presented. In using Newton …

An efficient implementation of generalized predictive control using a multilayer feedforward
neural network as the plant's nonlinear model is presented. By using Newton-Raphson as …

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 proposes a new approach for constrained multivariable predictive control based
on the use of a recurrent neural network as a non-linear prediction model of the plant under …