Model-Based Adaptive Control of Fed-Batch Fermentation Process with the Substrate Consumption Rate Application

Czeczot, Jacek

doi:10.1016/s1474-6670(17)35962-1

Cited by 11 publications

(17 citation statements)

References 11 publications

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“…1 was compared with the B-BAController based on the model (9). However, apart from simple comparison of two control strategies, we also decided to investigate the influence of the B-BAC tuning method on its closed loop performance.…”

Section: Resultsmentioning

confidence: 99%

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Improving efficiency of pH control by balance-based adaptive control application

Stebel¹,

Czeczot²,

Metzger³

2013

Archives of Control Sciences

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This paper deals with the efficient control of the pH process. Considering the PI + gain scheduling (PI+GS) controller as the benchmark and its control performance as the base, we investigate experimentally the overall improvement in the control performance obtained by the application of the Balance-Based Adaptive Controller (B-BAC), which requires only the measurement data of the flow rates and pH values. The improvement of the control efficiency is investigated not only in terms of the controlled variable performance but also in terms of the manipulated variable performance considered as the considerable control cost. The application of the B-BAController can ensure lower controlled pH variability at the price of the control effort similar to the PI+GS approach and thus it can improve the overall efficiency of pH control. The second important contribution is the experimental validation of the very simple and intuitive tuning procedure for the B-BAController.

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Section: Resultsmentioning

confidence: 99%

“…The value ofR Y denotes the on-line estimate of the unknown parameter R Y that is calculated by the scalar form of the Weighted Recursive Least-Squares (WRLS) method on the basis of the discretized model (1) [9]. After defining the auxiliary variable w i :…”

Section: B-bac Control Strategymentioning

confidence: 99%

Improving efficiency of pH control by balance-based adaptive control application

Stebel¹,

Czeczot²,

Metzger³

2013

Archives of Control Sciences

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“…Dochain & Vanrolleghem, 2001) and then transformed into the single dynamic equation that successively can be simplified to the desired form. The second method is directly based on the very general mass or energy conservation considerations written for the whole system in the form of the general mass or heat balance equation (Czeczot, 1998(Czeczot, , 2001(Czeczot, , 2006(Czeczot, , 2006a(Czeczot, , 2008, (Czeczot et al, 2010). Then, after simple recalculations that require only basic knowledge on the physical modelling of dynamic systems, this final dynamic equation can be rewritten to define the known part of Equation (1).…”

Section: Synthesis Of the Nonlinear B-bacmentioning

confidence: 99%

“…Due to the presence of the measurement noise represented by the additive error ε, Equation (2) cannot be directly applied to calculate the real value of R Y , so the estimation procedure based on the weighted recursive least-squares (WRLS) method is suggested to minimise the influence of this noise on the estimation accuracy (Czeczot, 1998 (2), the linear equation affine to the unknown parameterR Y with the constant regressor (−T R ) is obtained, and consequently the scalar discrete-time form of the well-known recursive estimation can be suggested:…”

Section: Synthesis Of the Nonlinear B-bacmentioning

confidence: 99%

General tuning procedure for the nonlinear balance-based adaptive controller

Stebel

Czeczot

Laszczyk

2013

International Journal of Control

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This paper presents the intuitive and ready-to-use, general procedure for tuning the balance-based adaptive controller (B-BAC) based on its equivalence to the controller with PI term and with additional improvements shown for the linearised approximation of the dynamics of the nonlinear controlled process. The simple formulas are suggested to calculate the B-BAC tunings based on the PI tunings determined by any PI tuning procedure chosen accordingly to the desired closed-loop performance. This methodology is verified by comparing the closed-loop performance of the equivalently tuned B-BAC and PI/PI + feedforward controllers under the same scenario, both by the simulation and practical experiments.

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“…This model accounts for disturbances influence but it does not includes any information about nonminimum-phase behavior, even in the form of the approximated dead time. These simplified models then stand as a basis for synthesis of two simple model-based strategies: Balance-Based Adaptive Controller (B-BAC) [2], [3] and Predictive Functional Controller (PFC) [4], [11]. Because both controllers are based on relatively simple models, they also have simple form that can be easily implemented in every industrial automation equipment, such as PLC.…”

Section: Introductionmentioning

confidence: 99%

Nonminimum-phase process control with controllers based on simplified modeling

Laszczyk

Czeczot

2012

2012 IEEE International Conference on Control Applications

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This paper deals with the control of the example heating-cooling nonminimum-phase system based on its simplified modeling. Two models are considered: FOPDT (First Order + Dead Time) approximation of the step response and simplified first order physical model. On the basis of these models, two model-based control strategies are derived: Balance-Based Adaptive Controller (B-BAC) and Predictive Functional Controller (PFC). They are implemented as virtual controllers and their control performance is verified by simulation in the presence of significant modeling inaccuracies. The results show the superiority of B-BAC and PFC over conventional PI control.

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Model-Based Adaptive Control of Fed-Batch Fermentation Process with the Substrate Consumption Rate Application

Cited by 11 publications

References 11 publications

Improving efficiency of pH control by balance-based adaptive control application

Improving efficiency of pH control by balance-based adaptive control application

General tuning procedure for the nonlinear balance-based adaptive controller

Nonminimum-phase process control with controllers based on simplified modeling

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