A Fed-Batch Fermentation Process Identification and Direct Adaptive Neural Control with Integral Term

Abstract

A nonlinear mathematical model of a feed-batch fermentation process of Bacillus thuringiensis (Bt.), is derived. The obtained model is validated by experimental data. Identification and direct adaptive neural control systems with and without integral term are proposed. The system contains a neural identifier and a neural controller, based on the recurrent trainable neural network model. The applicability of the proposed direct adaptive neural control system of both proportional and integral-term direct adaptive neural control schemes is confirmed by comparative simulation results, also with respect to the (-tracking control, which exhibit good convergence, but the I-term control could compensate a constant offset and proportional controls could not.