Multiple model predictive control of the fed-batch reactor

Abstract

In this work the use of multiple model predictive control (MMPC) for the control of batch processes is studied. The main idea is based on development of the local linear models for the whole operating range of the controlled process. The local models are identified from measured data using clustering and quadratic programming. The nonlinear plant is then approximated by a set of locally valid submodels, which are smoothly connected using the validity function. The approach is illustrated by a simulation study of a fed-batch process for the synthesis of hexyl monoester maleic acid.