An analytical predictive control law for a class of nonlinear processes

Many processes in the chemical industry have modest nonlinearities; i.e., l inear dynamics play a dominant role in governing the process output behavio r in the operating range of interest, but the linearization errors may be s ignificant. For these types of processes, linear-based control may yield a poor performance, while nonlinear-based control results in computation comp lexity. We propose to model this type of process with a composite model con sisting of a linear model (LM) and a multilayered feedforward neural networ k (MFNN). The LM is used to capture the Linear dynamics, while the MFNN is employed to predict the LM's residual errors, i.e., the process nonlinearit ies. Effective off-line and on-line al,algorithms are proposed for the iden tification of the composite model. With this model structure, it is shown t hat a simple analytical predictive control law can be formulated to control a nonlinear process. Simulation examples are also given to illustrate the effectiveness of the model identification and the proposed predictive contr ol.