An adaptive neural control of a fed-batch fermentation processes

Abstract

A nonlinear mathematical model of a feed-batch fermentation process of Bacillus thuringiensis (Bt.) is derived and a theorem proof of the existence of positive solution of the obtained model is done. The obtained model is validated by experimental data. An identification and adaptive neural control scheme of the system, represented by a neural identifier and a neural controller, based on the recurrent trainable neural network model, is proposed. The applicability of the proposed adaptive control scheme is confirmed by simulation results, which exhibits a good convergence.