Optimal control of a continuous bioreactor using an empirical nonlinear model

TLDR: In this article, a nonlinear model-based controller is developed to regulate the cell biomass exit concentration of a continuous-flow bioreactor by manipulating the dilution rate, which is shown to be nominally stable over the manipulated variable range [0.941, 0.999]h -1 using the structured singular value.

Abstract: Nonlinear model-based controllers are developed to regulate the cell biomass exit concentration of a continuous-flow bioreactor by manipulating the dilution rate. Plant-friendly input sequences are used to identify a second-order Volterra series model from a virtual plant. A Volterra-Laguerre model is produced by projection onto the orthonormal Laguerre basis functions. A partitioned nonlinear inverse (PNLI) controller is synthesized and is shown to be nominally stable over the manipulated variable range [0.941, 0.999]h -1 using the structured singular value. A referenced-based switching algorithm is incorporated to improve the robustness and stability characteristics of the closed-loop system. Nonlinear model predictive control (NMPC) alleviates the need for the switching controller, and an analytical NMPC solution incorporating recursive least squares avoids entrapment in local objective function minima. This controller offers optimum tracking for unreachable setpoints as well as tracking of the constrained local minimum for input-magnitude-constrained problems modeled by second-order Volterra-Laguerre systems.