A modular approach to constraint satisfaction under uncertainty - with application to bioproduction systems

Abstract

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin to minimize the cost of violating soft constraints due to uncertainty and disturbances. The module can be combined with any controller and is based on modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated by a simple heater example and a nonlinear and time-varying example in penicillin fed-batch production optimization.