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Complexity
Volume 2017, Article ID 9391879, 16 pages
https://doi.org/10.1155/2017/9391879
Research Article

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

1Instituto de Ingeniería Química, Universidad Nacional de San Juan, CONICET, Av. Lib. San Martín Oeste, 1109 San Juan, Argentina
2Instituto de Automática, Universidad Nacional de San Juan, CONICET, Av. Lib. San Martín Oeste, 1109 San Juan, Argentina
3Facultad Regional Santa Fe, Universidad Tecnológica Nacional, CONICET, Lavaisse 610, Santa Fe, Argentina
4Instituto de Desarrollo Tecnológico para la Industria Química (INTEC (UNL-CONICET)), Güemes, 3450 Santa Fe, Argentina

Correspondence should be addressed to Mario Serrano; ra.ude.jsnu.if@onarrese

Received 15 June 2017; Accepted 9 July 2017; Published 5 September 2017

Academic Editor: Chenguang Yang

Copyright © 2017 Santiago Rómoli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.