Linearized min‐max robust model predictive control: Application to the control of a bioprocess

Abstract

Summary This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameter uncertainties. First, this work focuses on the design of a robust nonlinear model predictive control (RNMPC) law subject to model parameter uncertainties implying solving a min‐max optimization problem. Secondly, a new approach is proposed, consisting in relating the min‐max problem to a more tractable optimization problem based on the use of linearization techniques to ensure a good trade‐off between tracking accuracy and computation time. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC and nonlinear model predictive controllers. Its efficiency and its robustness against parameter uncertainties and/or perturbations are illustrated through numerical results.

Control strategy for biopharmaceutical production by model predictive control

“…These advancements in robust MPC techniques contribute to the development of control strategies that effectively address uncertainties, ensuring stability and reliable performance within a specified range of model variations. By incorporating these robustness‐enhancing methods, engineers can strengthen the capabilities of MPC and effectively tackle the challenges posed by uncertain process dynamics 140,141 …”

Advances in bioreactor control for production of biotherapeutic products

“…Application of mechanistic models for bioreactor control has been well articulated in the literature for various objectives like process optimization, design, control, and scale‐up (Benattia et al, 2020; Glen et al, 2018; Knappert et al, 2020; Lisa et al 2017; Narayanan et al, 2020). Recently, mechanistic model was proposed for controlling temperature and implementing a reactor input−output flow rate strategy to maximize mAb productivity (Kumar et al, 2022).…”

Bioprocess Control: Current Progress and Future Perspectives

“…Such models can be mathematical, statistical, or empirical. Effective monitoring and control of processes through the use of mathematical models has been demonstrated by multiple researchers [25,30,89]. One such framework named parametric optimization and control (PAROC), which has been implemented for biopharmaceutical purification process, was developed using model-based control techniques.…”