Subspace Identification-Based Modeling and Control of Batch Particulate Processes

Abstract

This paper addresses the problem of subspace-based model identification and predictive control of particulate process subject to uncertainty and time-varying parameters. To this end, subspace identification techniques are first adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open-loop policy, as well as a traditional trajectory tracking policy, using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints, as well as robustness to uncertainty.