In-Hyoup Song

A linear input/output data-based predictive control with integral action property

Abstract A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step using subspace identification method. However, the state estimation in subspace identification gives a biased estimate and there exists a possibility of model mismatch when the controller is applied to a nonlinear process To overcome such difficulties, we add integral action to the controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation (BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor, the proposed controller demonstrates an improved control performance.

Control of a Continuous Polymerization Reactor by Wiener Input/Output Data-Based Predictive Controller with Direct Inverse Identification

ABSTRACT This article reports an experimental study for the identification and predictive control of a continuous methyl methacrylate (MMA) solution polymerization reactor. The Wiener model was introduced to identify the polymerization reactor in a more efficient manner than the conventional methods of Wiener model identification. In particular, the method of subspace identification was employed and the inverse of the nonlinear part was directly identified. The input variables in this work were the jacket inlet temperature and the feed flow rate, while the monomer conversion and the weight average molecular weight were selected as the output variables. On the basis of the identified model a Wiener-type input/output data-based predictive controller was designed and applied to the property control of polymer product in the continuous MMA polymerization reactor by conducting an on-line digital control experiment with online densitometer and viscometer. Despite the complex and nonlinear characteristics of the polymerization reactor, the proposed controller was found to perform satisfactorily for property control in the multiple-input multiple-output system with input constraints for both set-point tracking and disturbance rejection. This was also confirmed by simulation results.