STRATEGIES FOR MODELING AND CONTROL OF NONLINEAR CHEMICAL PROCESSES USING NEURAL NETWORKS

A nonlinear predictive control framework is presented, in which nonlin ear processes are modeled using neural networks. Several important iss ues concerning the modeling of nonlinear processes using neural networ ks are treated, with the emphasis placed on the convergence of neural networks to desired steady states. For nonlinear process predictive co ntrol where the neural network model is employed, an important case is examined. A typical nonlinear process, pH control problem, is taken a s a case study to demonstrate the-proposed approach, some significant results are given. (C) 1998 Elsevier Science Ltd. All rights reserved.