info:eu-repo/semantics/article
Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
Fecha
2017-07Registro en:
Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco Guido; Vega, Jorge Ruben; Ortiz, Oscar; et al.; Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor; Hindawi Publishing Corporation; Complexity; 2017; 7-2017; 1-16; 9391879
1076-2787
1099-0526
CONICET Digital
CONICET
Autor
Rómoli, Santiago
Serrano, Mario Emanuel
Rossomando, Francisco Guido
Vega, Jorge Ruben
Ortiz, Oscar
Scaglia, Gustavo Juan Eduardo
Resumen
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.