18 research outputs found
Control of a non-isothermal continuous stirred tank reactor by a feedback–feedforward structure using type-2 fuzzy logic controllers
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
Non linear control of glycaemia in type 1 diabetic patients
A fuzzy controller for the closed loop control, by insulin infusion of glycaemia in type 1 diabetic patients is proposed. The controller uses type-2 fuzzy sets. The controller was tested in simulation using a complex nonlinear model of the glucose metabolism. Simulation results confirm the effectiveness and the robustness of the type-2 fuzzy logic controller. The design of the controller uses an optimization method based on genetic algorithms. This makes the type-2 fuzzy controller more efficient and faster than a fuzzy controller with type-1 fuzzy sets, allowing a more accurate control of the glucose in the blood
Adaptive Type-2 Fuzzy Logic Control of a Bioreactor
Two 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
Abstract—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
Control of a Non-isothermal CSTR by Type-2 Fuzzy Logic Controllers
The paper describes the application of a type-2 fuzzy logic
controller to a non-isothermal continuous stirred tank reactor (CSTR)
characterized by the presence of saddle node and Hopf bifurcations, and its
performance compared with a type-1 fuzzy logic controller performance. A full
analysis of the uncontrolled CSTR dynamic was carried out and used for the
feedback-feedforward fuzzy controllers development. Simulation results
confirmed the effectiveness and the robustness of the type-2 FLCs which
outperform their type-1 counterparts, particularly when uncertainties are present
in the system
Adaptive Type-2 Fuzzy Control of Non-linear Systems
The 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
Development of a predicitive type-2 neurofuzzy controller
A 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