Abstract
Uncertainties inherent in biological systems make control of continuous fermenters a challenging task. This work proposes to integrate the quasi-infinite horizon nonlinear model predictive control (NMPC) algorithm, with an adaptive state and parameter estimator. As a result, the quasi-infinite horizon NMPC can be applied to systems with only part of the states measured. Moreover, the adaptive parameter estimation can update the uncertainty parameter online in the presence of plant-model mismatch. Two variations of target setting optimization problem are proposed to adjust the equilibrium points when they drift away due to the plant-model mismatch. The proposed method is applied to a fermentor with partial state feedback.
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