A practical robust control scheme for yeast fed-batch cultures – An experimental validation

Renard, F.; Wouwer, Alain Vande; Valentinotti, Sergio; Dumur, Didier

doi:10.1016/j.jprocont.2006.02.003

Cited by 42 publications

(15 citation statements)

References 5 publications

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“…According to (5), we have that |u*t < (j hi b )-l h, h (ai x + vi) +5(t) + yd (n) + KTT E+eU I (6) where lemod < eu, with eu being the upper bounds of emod Next, let the control input be given by…”

Section: B Supervisory Controller Designmentioning

confidence: 99%

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Supervised Adaptive PID Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

Chang

Tsai

2007

2007 IEEE International Fuzzy Systems Conference

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This paper proposes a supervised PID adaptive control scheme for unknown nonlinear systems to enhance system robustness in the face of external disturbances, variation in system parameters, and parameter drift in the adaptation law. The supervising controller is designed based on an on-line identified model in a fuzzily blended time-varying canonical form. The model largely simplifies the identification of the nonlinear plant, and the design of both the supervising controller and the adaptation law. Numerical studies of the tracking control of an uncertain Duffing-Holmes system demonstrate the effectiveness of the proposed control strategy.

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Section: B Supervisory Controller Designmentioning

confidence: 99%

“…In the subsequent derivation, we will choose Q such that The adaptation law of the PID adaptive controller, (24), is designed by using the modified projection method that ensures the boundedness of 6 (14), (18) and (26) are satisfied, the system is UUB stable.…”

Section: B Supervisory Controller Designmentioning

confidence: 99%

Supervised Adaptive PID Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

Chang

Tsai

2007

2007 IEEE International Fuzzy Systems Conference

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“…Desde el punto de vista de la ingeniería de control de procesos, el modo de operación fed-batch presenta grandes desafíos: sistema altamente no lineal, importante variación de los parámetros del modelo en función del tiempo, variabilidad en las condiciones iniciales, saturación de las acciones de control e influencia de perturbaciones externas, entre otros (Rani and Rao, 1999, Bayen and Mairet, 2013, Renard et al, 2006, Lee et al, 1999, Johnson, 1987, Daoutidis et al, 2016.…”

Section: Introductionunclassified

Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales

Fernández

Pantano

Godoy

et al. 2018

Rev. iberoam. autom. inform. ind.

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<p>En este trabajo se propone una estrategia de control en lazo cerrado para el seguimiento de perfiles óptimos previamente definidos para un bioproceso fed-batch. La mayor ventaja de este enfoque es que las acciones de control se calculan resolviendo un sistema de ecuaciones lineales, sin tener que linealizar el modelo matemático, lo que permite trabajar en cualquier rango. Además, se plantean tres técnicas para la sintonización de los parámetros del controlador diseñado. Primero se propone un método de Monte Carlo, el cual es un método probabilístico. En segundo lugar, se presenta una metodología basada en Algoritmos Genéticos, una técnica evolutiva de optimización. La tercera alternativa es el desarrollo de un Algoritmo Híbrido, diseñado a partir de la combinación de los dos métodos anteriores. En todos los casos, el objetivo es encontrar los parámetros del controlador que minimicen el error total de seguimiento de trayectorias. El desempeño del controlador se evalúa a través de simulaciones en condiciones normales de operación y frente a incertidumbre paramétrica, empleando los parámetros del controlador obtenidos.</p>

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“…Other approaches developed in the framework of yeast fed-batch cultures could also be transposed in view of the similarities in the overflow metabolism (ethanol production in yeast cultures and acetate production in bacteria cultures, when the respiratory capacity is exceeded). For instance, the robust RST strategy devised in [5,6] in principle could be applied.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control of Fed‐Batch Cultures of Escherichia coli

Tebbani¹,

Dumur²,

Hafidi³

et al. 2010

Chem Eng & Technol

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A strategy for controlling a fed-batch Escherichia coli culture is described to maintain the culture at the boundary between oxidative and oxido-fermentative regimes. A nonlinear predictive controller is designed to regulate the acetate concentration, constraining the feed rate to follow an optimal reference profile which maximizes the biomass growth. For the sake of simplicity and efficiency, the original problem is converted into an unconstrained nonlinear programming problem, solved by control vector parameterization techniques. The robustness of the structure is further improved by explicitly including the difference between system and model prediction. A robustness study based on a Monte Carlo approach is used to evaluate the performance of the proposed controller. This control law is finally compared to the generic model control strategy.

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A practical robust control scheme for yeast fed-batch cultures – An experimental validation

Cited by 42 publications

References 5 publications

Supervised Adaptive PID Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

Supervised Adaptive PID Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales

Nonlinear Predictive Control of Fed‐Batch Cultures of Escherichia coli

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