Optimal control of a continuous bioreactor using an empirical nonlinear model

Nonlinear model-based controllers are developed to regulate the cell biomas s exit concentration of continuous-flow bioreactor by manipulating the dilu tion rate. "Plant-friendly" input sequences are used to identify a second-o rder Volterra series model from a "virtual plant". A Volterra-Laguerre mode l is produced by projection onto the orthonormal Laguerre basis functions. A partitioned nonlinear inverse (PNLI) controller is synthesized and is sho wn to be nominally stable over the manipulated variable range [0.941, 0.999 ] h(-1) using the structured singular value. A referenced-based switching a lgorithm is incorporated to improve the robustness and stability characteri stics of the closed-loop system. Nonlinear model predictive control (NMPC) alleviates the need for the switching controller, and an analytical NMPC so lution incorporating recursive least squares avoids entrapment in local obj ective function minima. This controller offers optimum tracking for unreach able setpoints as well as tracking of the constrained local minimum for inp ut-magnitude-constrained problems modeled by second-order Volterra-Laguerre systems.