Abstract: Magneto-rheological (MR) fluid damper is a semiactive
control device that has recently received more attention by the
vibration control community. But inherent hysteretic and highly
nonlinear dynamics of MR fluid damper is one of the challenging
aspects to employ its unique characteristics. The combination of
artificial neural network (ANN) and fuzzy logic system (FLS) have
been used to imitate more precisely the behavior of this device.
However, the derivative-based nature of adaptive networks causes
some deficiencies. Therefore, in this paper, a novel approach that
employ genetic algorithm, as a free-derivative algorithm, to enhance
the capability of fuzzy systems, is proposed. The proposed method
used to model MR damper. The results will be compared with
adaptive neuro-fuzzy inference system (ANFIS) model, which is one
of the well-known approaches in soft computing framework, and two
best parametric models of MR damper. Data are generated based on
benchmark program by applying a number of famous earthquake
records.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
Abstract: Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.
Abstract: The efficient use of available licensed spectrum is
becoming more and more critical with increasing demand and usage
of the radio spectrum. This paper shows how the use of spectrum as
well as dynamic spectrum management can be effectively managed
and spectrum allocation schemes in the wireless communication
systems be implemented and used, in future. This paper would be an
attempt towards better utilization of the spectrum. This research will
focus on the decision-making process mainly, with an
assumption that the radio environment has already been sensed and
the QoS requirements for the application have been specified either
by the sensed radio environment or by the secondary user itself. We
identify and study the characteristic parameters of Cognitive Radio
and use Genetic Algorithm for spectrum allocation. Performance
evaluation is done using MATLAB toolboxes.
Abstract: One of the problems in fault diagnosis of transformer
based on dissolved gas, is lack of matching the result of fault
diagnosis of different standards with the real world. In this paper, the
result of the different standards is analyzed using fuzzy and the result
is compared with the empirical test. The comparison between the
suggested method and existing methods indicate the capability of the
suggested method in on-line fault diagnosis of the transformers. In
addition, in some cases the existing standards are not able to
diagnose the fault. In theses cases, the presented method has the
potential of diagnosing the fault. The information of three
transformers is used to the show the capability of the suggested
method in diagnosing the fault. The results validate the capability of
the presented method in fault diagnosis of the transformer.
Abstract: Fuel cells have become one of the major areas of
research in the academia and the industry. The goal of most fish
farmers is to maximize production and profits while holding labor
and management efforts to the minimum. Risk of fish kills, disease
outbreaks, poor water quality in most pond culture operations,
aeration offers the most immediate and practical solution to water
quality problems encountered at higher stocking and feeding rates.
Many units of aeration system are electrical units so using a
continuous, high reliability, affordable, and environmentally friendly
power sources is necessary. Aeration of water by using PEM fuel cell
power is not only a new application of the renewable energy, but
also, it provides an affordable method to promote biodiversity in
stagnant ponds and lakes. This paper presents a new design and
control of PEM fuel cell powered a diffused air aeration system for a
shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence
(AI) techniques control is used to control the fuel cell output power
by control input gases flow rate. Moreover the mathematical
modeling and simulation of PEM fuel cell is introduced. A
comparison study is applied between the performance of fuzzy logic
control (FLC) and neural network control (NNC). The results show
the effectiveness of NNC over FLC.
Abstract: Active power filter continues to be a powerful tool to control harmonics in power systems thereby enhancing the power quality. This paper presents a fuzzy tuned PID controller based shunt active filter to diminish the harmonics caused by non linear loads like thyristor bridge rectifiers and imbalanced loads. Here Fuzzy controller provides the tuning of PID, based on firing of thyristor bridge rectifiers and variations in input rms current. The shunt APF system is implemented with three phase current controlled Voltage Source Inverter (VSI) and is connected at the point of common coupling for compensating the current harmonics by injecting equal but opposite filter currents. These controllers are capable of controlling dc-side capacitor voltage and estimating reference currents. Hysteresis Current Controller (HCC) is used to generate switching signals for the voltage source inverter. Simulation studies are carried out with non linear loads like thyristor bridge rectifier along with unbalanced loads and the results proved that the APF along with fuzzy tuned PID controller work flawlessly for different firing angles of non linear load.
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Fuzzy logic approach is used in this study to predict
the tractive performance in terms of traction force, and motion
resistance for an intelligent air cushion track vehicle while it operates
in the swamp peat. The system is effective to control the intelligent
air –cushion system with measuring the vehicle traction force (TF),
motion resistance (MR), cushion clearance height (CH) and cushion
pressure (CP). Sinkage measuring sensor, magnetic switch, pressure
sensor, micro controller, control valves and battery are incorporated
with the Fuzzy logic system (FLS) to investigate experimentally the
TF, MR, CH, and CP. In this study, a comparison for tractive
performance of an intelligent air cushion track vehicle has been
performed with the results obtained from the predicted values of FLS
and experimental actual values. The mean relative error of actual and
predicted values from the FLS model on traction force, and total
motion resistance are found as 5.58 %, and 6.78 % respectively. For
all parameters, the relative error of predicted values are found to be
less than the acceptable limits. The goodness of fit of the prediction
values from the FLS model on TF, and MR are found as 0.90, and
0.98 respectively.
Abstract: This paper presents an automatic feature recognition
method based on center-surround difference detecting and fuzzy logic
that can be applied in ground-penetrating radar (GPR) image
processing. Adopted center-surround difference method, the salient
local image regions are extracted from the GPR images as features of
detected objects. And fuzzy logic strategy is used to match the
detected features and features in template database. This way, the
problem of objects detecting, which is the key problem in GPR image
processing, can be converted into two steps, feature extracting and
matching. The contributions of these skills make the system have the
ability to deal with changes in scale, antenna and noises. The results of
experiments also prove that the system has higher ratio of features
sensing in using GPR to image the subsurface structures.
Abstract: This paper presents an intelligent speed control
system based on fuzzy logic for a voltage source PWM inverter-fed
indirect vector controlled induction motor drive. Traditional indirect
vector control system of induction motor introduces conventional PI
regulator in outer speed loop; it is proved that the low precision of the
speed regulator debases the performance of the whole system. To
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of
the intelligent controller has been investigated through digital
simulation using MATLAB-SIMULINK package for different
operating conditions such as sudden change in reference speed and
load torque. The simulation results demonstrate that the performance
of the proposed controller is better than that of the conventional PI
controller.
Abstract: One of the major cause of eye strain and other
problems caused while watching television is the relative illumination between the screen and its surrounding. This can be
overcome by adjusting the brightness of the screen with respect to the surrounding light. A controller based on fuzzy logic is proposed
in this paper. The fuzzy controller takes in the intensity of light
surrounding the screen and the present brightness of the screen as input. The output of the fuzzy controller is the grid voltage corresponding to the required brightness. This voltage is given to CRT and brightness is controller dynamically. For the given test system data, different de-fuzzifier methods have been implemented and the results are compared. In order to validate the effectiveness of
the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time system. The simulations are
performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time
applications.
Abstract: Optimization of cutting parameters important in precision machining in regards to efficiency and surface integrity of the machined part. Usually productivity and precision in machining is limited by the forces emanating from the cutting process. Due to the inherent varying nature of the workpiece in terms of geometry and material composition, the peak cutting forces vary from point to point during machining process. In order to increase productivity without compromising on machining accuracy, it is important to control these cutting forces. In this paper a fuzzy logic control algorithm is developed that can be applied in the control of peak cutting forces in milling of spherical surfaces using ball end mills. The controller can adaptively vary the feedrate to maintain allowable cutting force on the tool. This control algorithm is implemented in a computer numerical control (CNC) machine. It has been demonstrated that the controller can provide stable machining and improve the performance of the CNC milling process by varying feedrate.
Abstract: In this paper, based on a novel synthesis, a set of new simplified circuit design to implement the linguistic-hedge operations for adjusting the fuzzy membership function set is presented. The circuits work in current-mode and employ floating-gate MOS (FGMOS) transistors that operate in weak inversion region. Compared to the other proposed circuits, these circuits feature severe reduction of the elements number, low supply voltage (0.7V), low power consumption (60dB). In this paper, a set of fuzzy linguistic hedge circuits, including absolutely, very, much more, more, plus minus, more or less and slightly, has been implemented in 0.18 mm CMOS process. Simulation results by Hspice confirm the validity of the proposed design technique and show high performance of the circuits.
Abstract: The performance of a type of fuzzy sliding mode control is researched by considering the nonlinear characteristic of a missile-target interception problem to obtain a robust interception process. The variable boundary layer by using fuzzy logic is proposed to reduce the chattering around the switching surface then is applied to the interception model which was derived. The performances of the sliding mode control with constant and fuzzy boundary layer are compared at the end of the study and the results are evaluated.
Abstract: This paper presents a speed fuzzy sliding mode
controller for a vector controlled induction machine (IM) fed by a
voltage source inverter (PWM).
The sliding mode based fuzzy control method is developed to
achieve fast response, a best disturbance rejection and to maintain a
good decoupling.
The problem with sliding mode control is that there is high
frequency switching around the sliding mode surface. The FSMC is
the combination of the robustness of Sliding Mode Control (SMC)
and the smoothness of Fuzzy Logic (FL). To reduce the torque
fluctuations (chattering), the sign function used in the conventional
SMC is substituted with a fuzzy logic algorithm.
The proposed algorithm was simulated by Matlab/Simulink
software and simulation results show that the performance of the
control scheme is robust and the chattering problem is solved.
Abstract: In this paper we present, propose and examine
additional membership functions for the Smoothing Transition
Autoregressive (STAR) models. More specifically, we present the
tangent hyperbolic, Gaussian and Generalized bell functions.
Because Smoothing Transition Autoregressive (STAR) models
follow fuzzy logic approach, more fuzzy membership functions
should be tested. Furthermore, fuzzy rules can be incorporated or
other training or computational methods can be applied as the error
backpropagation or genetic algorithm instead to nonlinear squares.
We examine two macroeconomic variables of US economy, the
inflation rate and the 6-monthly treasury bills interest rates.
Abstract: The anti-lock braking systems installed on vehicles
for safe and effective braking, are high-order nonlinear and timevariant.
Using fuzzy logic controllers increase efficiency of such
systems, but impose a high computational complexity as well. The
main concept introduced by this paper is reducing computational
complexity of fuzzy controllers by deploying problem-solution data
structure. Unlike conventional methods that are based on
calculations, this approach is based on data oriented modeling.
Abstract: Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables. In
order to effectively control the air-ratio, this paper presents a model
predictive fuzzy control algorithm based on online least-squares
support vector machines prediction model and fuzzy logic optimizer.
The proposed control algorithm was also implemented on a real car for
testing and the results are highly satisfactory. Experimental results
show that the proposed control algorithm can regulate the engine
air-ratio to the stoichiometric value, 1.0, under external disturbance
with less than 5% tolerance.
Abstract: Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system.