Trajectory tracking of a batch polymerization reactor based on input-output-linearization of a neural process model

Input-output-linearization via state feedback offers the potential to serve as a practical and systematic design methodology for nonlinear control sys tems. Nevertheless, its widespread use is delayed due to the fact that deve loping an accurate plant model based on physical principles is often too co stly and time consuming. Data-based modeling of dynamic systems using neura l networks offers a cost-effective alternative. This work describes the met hodology of input-output-linearization using neural process models and give s an extended simulative case study of its application to trajectory tracki ng of a batch polymerization reactor. (C) 2001 Elsevier Science Ltd. All ri ghts reserved.