Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
This work addresses thermal control in a specific industrial polymerization process, representing an incremental improvement by applying existing methods to a new experimental setup.
The authors tackled the problem of controlling a batch polymerization process by developing a nonlinear model predictive controller that uses a trajectory-linearized piecewise model and genetic algorithms to solve constrained optimization, achieving thermal trajectory tracking for MMA polymerization.
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.