Neural network based predictive control for nonlinear chemical process

Abstract

The paper presents a neural network based predictive control (NPC) strategy to control nonlinear chemical process or system. Multilayer perceptron neural network (MLP) is chosen to represent a Nonlinear autoregressive with exogenous signal (NARX) model of a nonlinear process. Based on the identified neural model, a generalized predictive control (GPC) algorithm is implemented to control the composition in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. Also an Instantaneous linearization based predictive control (IPC) strategy is discussed, in which an approximated linear model is extracted from nonlinear neural network by instantaneous linearization around operating points. The tracking performance of the NPC and IPC is tested using different amplitude step function as a reference signal on CSTR application and it is shown using simulation results, that the NPC strategy is more effective and robust than the IPC strategy.