Local Linear Modeling for Control of Batch Processes

Abstract

This paper contains of four contributions. An existing modeling framework for batch processes, which consists of a set of Finite Impulse Response (FIR) models, has been extended with the effect of initial conditions on batch evolution. It has been shown that an analogous modeling framework can be derived from a set of AutoRegressive models with eXogenous (ARX) inputs. The ARX models are superior to the FIR models with respect to parameterization and thus with respect to indentifiability as well as for capturing multivariable correlations. Based on the presented modeling framework, an existing Iterative Learning Control (ILC) algorithm has been extended with the effect of initial conditions and noisy measurements. The ILC algorithm is set up in a Model Predictive Control (MPC) framework. The presented tools and algorithms are demonstrated on simulated fed-batch yeast fermentations.