Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

• 作者: Masoud Abbaszadeh Reza Solgi
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 化学
• 关键词: Model PREDICTIVE Control Genetic ALGORITHMS POLYMERIZATION METHYL METHACRYLATE Parameter Identification
• 年，卷（期）: 2014,（1）
• 页码范围: 35-44
• 页数: 10页
• 分类号: O6

摘要

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.