A Predictive Neural Network-Based Cascade Control for pH Reactors

Abstract EN

This paper is concerned with the development of predictive neural network-based cascade control for pH reactors.

The cascade structure consists of a master control loop (fuzzy proportional-integral) and a slave one (predictive neural network).

The master loop is chosen to be more accurate but slower than the slave one.

The strong features found in cascade structure have been added to the inherent features in model predictive neural network.

The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior.

The parameters of predictive algorithm are determined using an optimization algorithm.

The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.