Input-output-linearization using neural process models

Input-output-linearization via state feedback offers the potential to serve as a practical and systematic design methodology for nonlinear control systems. Nevertheless, its widespread use is delayed due to the fact that developing an accurate plant model based on physical principles is often too costly and time consuming. Data-based dynamic modeling using neural networks offers a cost-effective alternative. The work describes the methodology of input-output-linearization using neural process models and gives an extended simulative case study of its application to trajectory tracking of a batch polymerization reactor