Nonlinear fuzzy control of a fed-batch reactor for penicillin production (original) (raw)
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The aim of the paper is to present the application of type-2 fuzzy logic controllers (T2FLCs) to the control of a fed-batch fermentation reactor in which the penicillin production is carried out. The performance of the control system using T2FLCs is compared by simulation with that of a control system using type-1 fuzzy logic controllers (T1FLCs). The non linear model used for the simulation study is an unstructured model characterized by the presence of non linearities, parameter uncertainty and measurement noise. Simulation results confirm the robustness of the T2FLC which shows a better performance than its type-1 counterpart particularly when uncertainties are present in the control system.
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Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than convent...
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The issues of synthesis of a fuzzy control system for ill-defined technological processes are considered. An effective algorithm for the synthesis of a fuzzy logic controller and a fuzzy system for automatic regulation of the temperature regime of a chemical reactor, invariant to parametric and external disturbances, is presented. The proposed synthesis algorithm for a fuzzy-logical proportional-integral-differential (PID)-controller is simple and allows you to use a standard form of description of linguistic variables and a minimum set of control rules. The synthesized fuzzy logic controller gives the entire automatic control system the ability to maintain the reactor temperature at a given level in the presence of external disturbances, as well as to qualitatively control the technological process with a wide range of changes in its parameters over time. The used methods of the theory of fuzzy logic and neural networks allow you to operate with linguistic fuzzy statements. The bases of the rules of logical inference of a fuzzy-logical regulator in the form of a Cartesian product of fuzzy sets with a membership function, which has a trapezoidal shape, have been formed. The results of modeling a fuzzylogic control system showed that if there is a noisy external disturbing signal in the system and its level changes up to 30%, as well as changes in the parameters of the control object (gain and constant time) up to 25% (in the direction of increasing and decreasing), the fuzzy system retains the properties of stability.
Adaptive type-2 fuzzy logic control of a bioreactor
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Control Engineering Practice, 2004
This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will make the automatization feasible and eliminate the need for human operator. FasArt and FasBack feature fast stable learning and good MIMO identification, which makes them suitable for the development of adaptive controllers and soft sensors. In this paper, these modules are evaluated by training the neuro-fuzzy systems first on simulated data and then applying the resulting IMC controllers to a simulated plant. Moreover, training the systems on data coming from a real pilot plant, and evaluating the controller performance on the same real plant. Results show that the trend of reference is captured, thus allowing high penicillin production. Moreover, soft sensors derived for biomass, viscosity and penicillin are very accurate. In addition, on-line adaptive capabilities were implemented and tested with FasBack, since this system presents learning guided by error minimization as new data samples arrive. With these features, adaptive IMC controllers can be implemented and are helpful when dynamics have been poorly learned or the plant parameters vary with time, since the performance of static models and controllers can be improved through adaptation.
—This paper has presented application and implementation of fuzzy logic controller (FLC) for feed chemical concentration process. It is desired to improve the chemical composition of a feed chemical concentration control system whose initial response was undesirable. In order to achieve the objective of the paper, the dynamic model of a typical chemical concentration with a feed-flow valve is obtained and presented in the form of a transfer function. A fuzzy logic controller is designed using fuzzy block of the Simulink. Simulations are performed in MATLAB/Simulink environment considering two cases. The first simulation was performed considering the case where the designed controller has not been added to the closed-loop. The second simulation was performed considering the case where the FLC has been included in the close-loop. The Simulation results obtained showed that the output composition performance of the process was largely improved and maintain steady state within the acceptable percentage of the output composition.
Control of a biochemical process using fuzzy approach
2017 21st International Conference on Process Control (PC)
The work deals with design and application of fuzzy controllers for a biochemical process. Fuzzy logic control based on the Takagi-Sugeno inference method has been applied for control of the baker's yeast fermentation. The advantage of the fuzzy control design is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers.
Control of the penicillin production using fuzzy neural networks
This paper addresses the control of a penicillin fermentation pilot plant using IMC strategies with modules based on FasArt neurofuzzy system. FasArt features fast stable learning and shows good MIMO identification, which makes it suitable for development of the modules in IMC. Experiments have been done training FasArt on real data and applying the controller to the pilot plant, and show that the trend of reference is captured, thus allowing high penicillin production. Other experiments have been aimed towards development of soft sensors of important variables using FasArt. Biomass, viscosity and penicillin production predictors are very accurate, and reveal that FasArt modules could be employed for fault detection, control with constraints or predictive control.
A control system that uses type-2 fuzzy logic controllers (FLC) is proposed for the control of a non-isothermal continuous stirred tank reactor (CSTR), where a first order irreversible reaction occurs and that is characterized by the presence of bifurcations. Bifurcations due to parameter variations can bring the reactor to instability or create new working conditions which although stable are unacceptable. An extensive analysis of the uncontrolled CSTR dynamics was carried out and used for the choice of the control configuration and the development of controllers. In addition to a feedback controller, the introduction of a feedforward control loop was required to maintain effective control in the presence of disturbances. Simulation results confirmed the effectiveness and the robustness of the type-2 FLC which outperforms its type-1 counterpart particularly when system uncertainties are present.