Robust model predictive control and fault handling of batch processes

Abstract

This work considers the control of batch processes subject to input constraints and model uncertainty with the objective of achieving a desired product quality. First, a computationally efficient nonlinear robust Model Predictive Control (MPC) is designed. The robust MPC scheme uses robust reverse-time reachability regions (RTRRs), which we define as the set of process states that can be driven to a desired neighborhood of the target end-point subject to input constraints and model uncertainty. A multilevel optimization-based algorithm to generate robust RTRRs for specified uncertainty bounds is presented. We then consider the problem of uncertain batch processes subject to finite duration faults in the control actuators. Using the robust RTRR-based MPC as the main tool, a robust safe-steering framework is developed to address the problem of how to operate the functioning inputs during the fault repair period to ensure that the desired end-point neighborhood can be reached upon recovery of the full control effort. The applicability of the proposed robust RTRR-based controller and safe-steering framework subject to limited availability of measurements and sensor noise are illustrated using a fed-batch reactor system. © 2010 American Institute of Chemical Engineers AIChE J, 2011