  Bioprocesses are often characterized by nonlinear and uncertain dynamics.
This poses particular challenges in the context of model predictive control
(MPC). Several approaches have been proposed to solve this problem, such as
robust or stochastic MPC, but they can be computationally expensive when the
system is nonlinear. Recent advances in optimal control theory have shown that
concepts from convex optimization, tube-based MPC, and difference of convex
functions (DC) enable stable and robust online process control. The approach is
based on systematic DC decompositions of the dynamics and successive
linearizations around feasible trajectories. By convexity, the linearization
errors can be bounded tightly and treated as bounded disturbances in a robust
tube-based MPC framework. However, finding the DC composition can be a
difficult task. To overcome this problem, we used a neural network with special
convex structure to learn the dynamics in DC form and express the uncertainty
sets using simplices to maximize the product formation rate of a cultivation
with uncertain substrate concentration in the feed. The results show that this
is a promising approach for computationally tractable data-driven robust MPC of
bioprocesses.
