Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization
Open Access
- 1 January 2014
- journal article
- Published by Scientific Research Publishing, Inc. in Journal of Intelligent Learning Systems and Applications
- Vol. 06 (01), 35-44
- https://doi.org/10.4236/jilsa.2014.61004
Abstract
In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Keywords
This publication has 1 reference indexed in Scilit:
- Genetic Algorithms + Data Structures = Evolution ProgramsPublished by Springer Science and Business Media LLC ,1996