Recurrent neural identification and control of a continuous bioprocess using the recursive Marquardt algorithm

The aim of this paper is to propose a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) algorithm of its learning capable to estimate the states and parameters of a highly nonlinear continuous fermentation bioprocess in noisy environment. The proposed KFRNN identifier is incorporated in a direct adaptive control scheme containing also feedback and feedforward recurrent neural controllers. The proposed control scheme is applied for real-time identification and control of continuous stirred tank bioreactor model, taken from the literature, where a fast convergence, noise filtering and low mean squared error of reference tracking were achieved.