Abstract
AbstractThe proposed on‐line adaptive optimization technique incorporates a priori knowledge in the form of approximate steady‐state models. The steady‐state geometric characteristics of the model are periodically recalculated using a Hammerstein system and recursive least squares. The algorithm is self‐tuning in the sense that it converges to the optimal performance provided that a matching condition is satisfied and that the data are persistently exited. Simulation and experimental studies performed on a continuous fermentation system have been conducted to illustrate the performance of the optimization algorithm and demonstrate the viability of adaptive extremum control.
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