A chemical reactor benchmark for parallel adaptive control using feedforward neural networks

Abstract

This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (continuous stirred tank reactor). Convergence of the identification error is investigated by Lyapunov's second method. The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm.