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Control of the Biodegradation of Mixed Wastes in a Continuous Bioreactor by a Type-2 Fuzzy Logic Controller
No full textThe paper describes the application of type-2 fuzzy logic control to a nonlinear system with bifurcations. A type-2 fuzzy logic controller is tested by simulation on a bioreactor with cell recycle that presents bifurcations. The process involves a pure culture of Pseudomonas putida containing phenol and glucose as carbon and energy sources and is characterized by two saddle-node bifurcations. Because of the bifurcational behaviour the system may become unstable also for further variation of process parameters. The simulation results show the validity of the proposed controller, that, compared with other controllers, has a higher performance in terms of robustness and response speed
Adaptive Type-2 Fuzzy Logic Control of a Bioreactor
Get PDFTwo adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and
compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the
growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity
and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive
controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the
ability of predictive control to predict future plant performance making use of a neural network model
of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI
controller, where the output scaling factor is adjusted online by fuzzy rules according to the current
trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used
as reference
Type-2 Fuzzy Control of a Bioreactor
Get PDFAbstract—In this paper the control of a bioprocess using
an adaptive type-2 fuzzy logic controller is proposed.
The process is concerned with the aerobic alcoholic
fermentation for the growth of Saccharomyces Cerevisiae
a n d i s characterized by nonlinearity and parameter
uncertainty. Three type-2 fuzzy controllers heve been
developed and tested by simulation: a simple type-2
fuzzy logic controller with 49 rules; a type-2 fuzzyneuro-
predictive controller (T2FNPC); a t y p e -2 selftuning
fuzzy controller ( T2STFC). The T2FNPC
combines the capability of the type-2 fuzzy logic to
handle uncertainties, with the ability of predictive
control to predict future plant performance making use
of a neural network model of the non linear system. In
the T2STFC the output scaling factor is adjusted on-line
by fuzzy rules according to the current trend of the
controlled process. T h e advantage of the proposed
adaptive algorithms is to greatly decrease the number of
rules needed for the control reducing the computational
load and at same time assuring a robust control
Adaptive Type-2 Fuzzy Control of Non-linear Systems
No full textThe paper describes the development of two different
type-2 adaptive fuzzy logic controllers and their use for the
control of a non linear system that is characterized by the
presence of bifurcations and parameter uncertainty.
Although a type-2 fuzzy logic controller is able to handle the non
linearities and the uncertainties present in a system, its
robustness and effectiveness can be increased by the use of an
opportune adaptive algorithm. A simulation study was conducted
to compare the behavior of adaptive controllers with that of
simple type-1 and type-2 fuzzy logic controllers. The system to be
controlled, used for the simulation, is a continuous bioreactor for
the treatment of mixed wastes in which a culture of Pseudomonas
Putida is carried out while phenol and glucose are carbon and
energy sources. From simulations results it can be seen that both
adaptive controllers, but in particular the self tuning controller,
have a better performance being able to eliminate oscillations
that are present with basic fuzzy controllers
Adaptive Type-2 Fuzzy Logic Control of Non-Linear Processes
Get PDFThe main objective of this study is to provide a valid and effective approach for the design and development of an adaptive type-2 fuzzy controller (AT2FLC), based on the analysis of the nonlinear process dynamics and the use of an ANFIS technique for the optimization of the controller. The performance of the obtained AT2FLC, characterized by a few number of rules, is higher than the performance of a traditional type-2 fuzzy controller with a larger rule base. The proposed controller is particurarly suitable for the control of processes characterized by uncertainty and time varying parameters
Development of a predicitive type-2 neurofuzzy controller
No full textA controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership function parameters. The proposed controller is tested by simulation for the control of a bioreactor characterized by bifurcation and parameter uncertainty
Control of a nonlinear continuous bioreactor with bifurcation by a type-2 fuzzy logic controller
No full textThe object of this paper is the application of a type-2 fuzzy logic controller to a nonlinear system that presents bifurcations. A bifurcation can cause instability in the system or can create new working conditions
which, although stable, are unacceptable. The only practical solution for an efficient control is the use of high performance controllers that take into account the uncertainties of the process. A type-2
fuzzy logic controller is tested by simulation on a nonlinear bioreactor system that is characterized by a transcritical bifurcation. Simulation results show the validity of the proposed controllers in preventing the system from reaching bifurcation and instable or undesirable stable conditions
Stability analysis of type-2 fuzzy logic controllers
No full textThe application of the direct Lyapunov method to the stability analysis of systems
controlled by type-2 fuzzy logic controllers (FLC) is presented. The method is an
extension of a method proposed for type-1 fuzzy systems. It is usually applied to
systems described by state equations and controlled by fuzzy controllers using state
variables as inputs but has been extended to controllers that have the error and the
integral of error of the controlled variable as inputs.
The proposed method allows to modify the controller rule base so that the controlled
system is stable in the operating range defined by the manipulative variable constraints.
The method is applied to the stability analysis of a bioreactor and of a CSTR controlled
by type-2 FLCs
Control of a non-isothermal continuous stirred tank reactor by a feedback–feedforward structure using type-2 fuzzy logic controllers
Get PDFA 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
Experimental Comparison of Type-1 and Type-2 Fuzzy Logic Controllers for the Control of Level and Temperature in a Vessel
Get PDFThe objective of this experimental study is to compare the performance of type-1 and type-2 fuzzy logic controllers on a real system where the control of liquid level and temperature are considered. By the use of genetic algorithms it is possible to optimize the fuzzy sets of each fuzzy controller assuring high control performance. The experimental results show that a better control in terms of robustness can be achieved by type-2 fuzzy logic controllers
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