Data-driven control of the chemostat using the koopman operator theory

The chemostat is widely used as a laboratory pilot for bioprocess studies. Chemostat models are nonlinear and rarely used in modern control experiments. For a data-driven control strategy, we use the Koopman operator approach to derive a linear model for a simple chemostat with one substrate and one biomass, using only the chemostat's input-output data. For chemostat control, we use the linear Koopman model to develop a MPC controller. The linear Koopman model best fits chemostat data compared to the local linearization-based model. In addition, the MPC based on the Koopman model gives very satisfying results compard with a linear MPC controller when applied to control the chemostat. The results are gained for a large space of initial conditions when chemostat control is usually limited.