Tracking control of concentration profiles in a fed-batch bioreactor using a linear algebra methodology

Rómoli, Santiago; Serrano, Mario Emanuel; Ortiz, Oscar A.; Vega, Jorge Rubén; Scaglia, Gustavo

doi:10.1016/j.isatra.2015.01.002

Cited by 19 publications

(17 citation statements)

References 32 publications

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“…Equations (9a) and (9b) suggest a direct effect of the additive uncertainty on the tracking error. Therefore, the presence of an uncertainty avoids a convergence of tracking errors to zero (see Theorem 1 of [30]). …”

Section: Complexitymentioning

confidence: 99%

“…Once the integral of the error has been added, the control action is calculated following the same design procedure based on linear algebra presented in [30]. Therefore, the new control law can be obtained through the least squares procedure as…”

Section: Remarkmentioning

confidence: 99%

“…Based on a linear algebra methodology, a control strategy has been applied for trajectory tracking in a bioprocess [30]. The technique was used to calculate the control actions that ensure the tracking of independently determined optimal trajectories of some process variables.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach is evaluated onto a bioprocess originally modeled by [31], with the optimal profiles determined by [32] in absence of model errors. Brief descriptions of the fed-batch bioreactor and the controller derived in [30] are presented in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with [30], the proposed approach prevents the polynomial uncertainties from affecting the tracking control. To achieve this aim, an integrative term is incorporated in the linear algebra methodology originally proposed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Complexitymentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%