Successive Linearization based Stochastic Model Predictive Control for batch processes described by DAEs

Abstract

This paper presents a formulation of Stochastic Model Predictive Control (SMPC) for control of batch processes modeled by nonlinear Differential algebraic Equations (DAEs). In presence of additive stochastic disturbances, states become stochastic variables and measurements are also corrupted with noise. Hence, to obtain refined state estimates we employ DAE-Extended Kalman Filter (EKF). Linear prediction model obtained by successive linearization is used in the SMPC formulation. Our proposed SMPC solves a chance constrained optimization problem in receding horizon window to calculate future control moves with pre-specified degree of constraint violation for trajectory tracking. Further, the probabilistic constraints are converted to hard constraints by accommodating necessary back-off. We present a simulation case study of batch transesterification reactor modeled by non-linear DAEs to validate the proposed SMPC framework.