Abstract: Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.
Abstract: In order to have stable and high performance of direct torque and flux control (DTFC) of double star induction motor drive (DSIM), proper on-line adaptation of the stator resistance is very important. This is inevitably due to the variation of the stator resistance during operating conditions, which introduces error in estimated flux position and the magnitude of the stator flux. Error in the estimated stator flux deteriorates the performance of the DTFC drive. Also, the effect of error in estimation is very important especially at low speed. Due to this, our aim is to overcome the sensitivity of the DTFC to the stator resistance variation by proposing on-line fuzzy estimation stator resistance. The fuzzy estimation method is based on an on-line stator resistance correction through the variations of the stator current estimation error and its variations. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of the suggested algorithm control is to avoid the drive instability that may occur in certain situations and ensure the tracking of the actual stator resistance. The validity of the technique and the improvement of the whole system performance are proved by the results.
Abstract: Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.
Abstract: This paper demonstrates dynamic performance evaluation of load frequency control (LFC) with different intelligent techniques. All non-linearities and physical constraints have been considered in simulation studies such as governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics. The conventional integral time absolute error has been considered as objective function. The design problem is formulated as an optimisation problem and particle swarm optimisation (PSO), bacterial foraging optimisation algorithm (BFOA) and differential evolution (DE) are employed to search optimal controller parameters. The superiority of the proposed approach has been shown by comparing the results with published fuzzy logic control (FLC) for the same interconnected power system. The comparison is done using various performance measures like overshoot, undershoot, settling time and standard error criteria of frequency and tie-line power deviation following a step load perturbation (SLP). It is noticed that, the dynamic performance of proposed controller is better than FLC. Further, robustness analysis is carried out by varying the time constants of speed governor, turbine, tie-line power in the range of +40% to -40% to demonstrate the robustness of the proposed DE optimized PID controller.
Abstract: Mobile ad hoc network (MANET) is a self-configuring network of mobile node connected without wires. A Fuzzy Logic Based Collaborative watchdog approach is used to reduce the detection time of misbehaved nodes and increase the overall truthfulness. This methodology will increase the secure efficient routing by detecting the Black Holes attacks. The simulation results proved that this method improved the energy, reduced the delay and also improved the overall performance of the detecting black hole attacks in MANET.
Abstract: In this paper, type-2 fuzzy logic control (T2FLC) and neuro-fuzzy control (NFC) for a doubly fed induction generator (DFIG) based on direct power control (DPC) with a fixed switching frequency is proposed for wind generation application. First, a mathematical model of the doubly-fed induction generator implemented in d-q reference frame is achieved. Then, a DPC algorithm approach for controlling active and reactive power of DFIG via fixed switching frequency is incorporated using PID. The performance of T2FLC and NFC, which is based on the DPC algorithm, are investigated and compared to those obtained from the PID controller. Finally, simulation results demonstrate that the NFC is more robust, superior dynamic performance for wind power generation system applications.
Abstract: Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.
Abstract: For the dearth of reliable cardinal numerical data, the linked phenomena in productivity indices such as operational costs and company turnovers, etc. could not be investigated. This would not give us insight to the root of productivity problems at unique sites. So, ordinal ranking by professionals who were most directly involved with construction sites was applied for Kendall’s concordance. Responses gathered from independent architects, builders/engineers, and quantity surveyors were herein analyzed. They were responses based on factors that affect sites productivity, and these factors were categorized as head office factors, resource management effectiveness factors, motivational factors, and training/skill development factors. It was found that productivity is low and has to be improved in order to facilitate Nigerian efforts in bridging its infrastructure deficit. The significance of this work is underlined with the Kendall’s coefficient of concordance of 0.78, while remedial measures must be emphasized to stimulate better productivity. Further detailed study can be undertaken by using Fuzzy logic analysis on wider Delphi survey.
Abstract: The growing importance of reputation in building enterprise value and achieving long-term competitive advantage creates the need for its measurement and evaluation for the management purposes (effective reputation and its risk management). The paper presents practical application of self-developed corporate reputation assessment model from the viewpoint of stock market investors. The model has a pioneer character and example analysis performed for selected industry is a form of specific test for this tool. In the proposed solution, three aspects - informational, financial and development, as well as social ones - were considered. It was also assumed that the individual sub-criteria will be based on public sources of information, and as the calculation apparatus, capable of obtaining synthetic final assessment, fuzzy logic will be used. The main reason for developing this model was to fulfill the gap in the scope of synthetic measure of corporate reputation that would provide higher degree of objectivity by relying on "hard" (not from surveys) and publicly available data. It should be also noted that results obtained on the basis of proposed corporate reputation assessment method give possibilities of various internal as well as inter-branch comparisons and analysis of corporate reputation impact.
Abstract: This paper contributes to the literature by updating the analysis of the impact of the recent oil prices fall on the renewable energy (RE) industry and deployment. The research analysis uses the Renewable Energy Industrial Index (RENIXX), which tracks the world’s 30 largest publicly traded companies and oil prices daily data from January 2003 to March 2016. RENIXX represents RE industries developing solar, wind, geothermal, bioenergy, hydropower and fuel cells technologies. This paper tests the hypothesis that claims high oil prices encourage the substitution of alternate energy sources for conventional energy sources. Furthermore, it discusses RENIXX performance behavior with respect to the governments’ policies factor that investors should take into account. Moreover, the paper proposes a theoretical model that relates RE industry progress with oil prices and policies through the fuzzy logic system.
Abstract: Maximum Power Point Tracking (MPPT) has played a vital role to enhance the efficiency of solar photovoltaic (PV) power generation under varying atmospheric temperature and solar irradiation. However, it is hard to track the maximum power point using conventional linear controllers due to the natural inheritance of nonlinear I-V and P-V characteristics of solar PV systems. Fuzzy Logic Controller (FLC) is suitable for nonlinear system control applications and eliminating oscillations, circuit complexities present in the conventional perturb and observation and incremental conductance methods respectively. Hence, in this paper, FLC is proposed for tracking exact MPPT of solar PV power generation system under varying solar irradiation conditions. The effectiveness of the proposed FLC-based MPPT controller is validated through simulation and analysis using MATLAB/Simulink.
Abstract: Liquid level control of conical tank system is known to be a great challenge in many industries such as food processing, hydrometallurgical industries and wastewater treatment plant due to its highly nonlinear characteristics. In this research, an adaptive fuzzy PID control scheme is applied to the problem of liquid level control in a nonlinear tank process. A conical tank process is first modeled and primarily simulated. A PID controller is then applied to the plant model as a suitable benchmark for comparison and the dynamic responses of the control system to different step inputs were investigated. It is found that the conventional PID controller is not able to fulfill the controller design criteria such as desired time constant due to highly nonlinear characteristics of the plant model. Consequently, a nonlinear control strategy based on gain-scheduling adaptive control incorporating a fuzzy logic observer is proposed to accurately control the nonlinear tank system. The simulation results clearly demonstrated the superiority of the proposed adaptive fuzzy control method over the conventional PID controller.
Abstract: In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.
Abstract: This paper considers people’s driving skills
diagnosis under real driving conditions. In that sense, this research
presents an approach that uses GPS signals which have a direct
correlation with driving maneuvers. Besides, it is presented a novel
expert-driving-criteria approximation using fuzzy logic which
seeks to analyze GPS signals in order to issue an intelligent driving
diagnosis.
Based on above, this works presents in the first section the
intelligent driving diagnosis system approach in terms of its own
characteristics properties, explaining in detail significant
considerations about how an expert-driving-criteria approximation
must be developed. In the next section, the implementation of our
developed system based on the proposed fuzzy logic approach is
explained. Here, a proposed set of rules which corresponds to a
quantitative abstraction of some traffics laws and driving secure
techniques seeking to approach an expert-driving- criteria
approximation is presented.
Experimental testing has been performed in real driving
conditions. The testing results show that the intelligent driving
diagnosis system qualifies driver’s performance quantitatively with
a high degree of reliability.
Abstract: This study aims at being acquainted with the using the
body fat percentage (%BF) with body Mass Index (BMI) as input
parameters in fuzzy logic decision support system to predict properly
the lifted weight for students at weightlifting class lift according to
his abilities instead of traditional manner. The sample included 53
male students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28
cm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI)
23.42 ± 2.06 kg.m-2, fat mass (FM) = 9.96 ± 3.15 kg and fat
percentage (% BF) = 13.98 ± 3.51 %.) experienced the weightlifting
class as a credit and has variance at BW, Hgt and BMI and FM. BMI
and % BF were taken as input parameters in FUZZY logic whereas
the output parameter was the lifted weight (LW). There were
statistical differences between LW values before and after using
fuzzy logic (Diff 3.55± 2.21, P > 0.001). The percentages of the LW
categories proposed by fuzzy logic were 3.77% of students to lift 1.0
fold of their bodies; 50.94% of students to lift 0.95 fold of their
bodies; 33.96% of students to lift 0.9 fold of their bodies; 3.77% of
students to lift 0.85 fold of their bodies and 7.55% of students to lift
0.8 fold of their bodies. The study concluded that the characteristic
changes in body composition experienced by students when
undergoing weightlifting could be utilized side by side with the
Fuzzy logic decision support system to determine the proper
workloads consistent with the abilities of students.
Abstract: Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H2S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. New design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.
Abstract: This paper describes a simple way to control the speed
of PMBLDC motor using Fuzzy logic control method. In the
conventional PI controller the performance of the motor system is
simulated and the speed is regulated by using PI controller. These
methods used to improve the performance of PMSM drives, but in
some cases at different operating conditions when the dynamics of
the system also vary over time and it can change the reference speed,
parameter variations and the load disturbance. The simulation is
powered with the MATLAB program to get a reliable and flexible
simulation. In order to highlight the effectiveness of the speed control
method the FLC method is used. The proposed method targeted in
achieving the improved dynamic performance and avoids the
variations of the motor drive. This drive has high accuracy, robust
operation from near zero to high speed. The effectiveness and
flexibility of the individual techniques of the speed control method
will be thoroughly discussed for merits and demerits and finally
verified through simulation and experimental results for comparative
analysis.
Abstract: The paper presents a method in which the expert
knowledge is applied to fuzzy inference model. Even a less
experienced person could benefit from the use of such a system, e.g.
urban planners, officials. The analysis result is obtained in a very
short time, so a large number of the proposed locations can also be
verified in a short time. The proposed method is intended for testing
of locations of car parks in a city. The paper shows selected examples
of locations of the P&R facilities in cities planning to introduce the
P&R. The analyses of existing objects are also shown in the paper
and they are confronted with the opinions of the system users, with
particular emphasis on unpopular locations. The results of the
analyses are compared to expert analysis of the P&R facilities
location that was outsourced by the city and the opinions about
existing facilities users that were expressed on social networking
sites. The obtained results are consistent with actual users’ feedback.
The proposed method proves to be good, but does not require the
involvement of a large experts team and large financial contributions
for complicated research. The method also provides an opportunity to
show the alternative location of P&R facilities. Although the results
of the method are approximate, they are not worse than results of
analysis of employed experts. The advantage of this method is ease of
use, which simplifies the professional expert analysis. The ability of
analyzing a large number of alternative locations gives a broader
view on the problem. It is valuable that the arduous analysis of the
team of people can be replaced by the model's calculation. According
to the authors, the proposed method is also suitable for
implementation on a GIS platform.
Abstract: Brushless DC motors (BLDC) are widely used in
industrial areas. The BLDC motors are driven either by indirect ACAC
converters or by direct AC-AC converters. Direct AC-AC
converters i.e. matrix converters are used in this paper to drive the
three phase BLDC motor and it eliminates the bulky DC link energy
storage element. A matrix converter converts the AC power supply to
an AC voltage of variable amplitude and variable frequency. A
control technique is designed to generate the switching pulses for the
three phase matrix converter. For the control of speed of the BLDC
motor a separate PI controller and Fuzzy Logic Controller (FLC) are
designed and a hysteresis current controller is also designed for the
control of motor torque. The control schemes are designed and tested
separately. The simulation results of both the schemes are compared
and contrasted in this paper. The results show that the fuzzy logic
control scheme outperforms the PI control scheme in terms of
dynamic performance of the BLDC motor. Simulation results are
validated with the experimental results.
Abstract: Recent investigations have demonstrated the global
sea level rise due to climate change impacts. In this study, climate
changes study the effects of increasing water level in the strait of
Hormuz. The probable changes of sea level rise should be
investigated to employ the adaption strategies. The climatic output
data of a GCM (General Circulation Model) named CGCM3 under
climate change scenario of A1b and A2 were used. Among different
variables simulated by this model, those of maximum correlation
with sea level changes in the study region and least redundancy
among themselves were selected for sea level rise prediction by using
stepwise regression. One of models (Discrete Wavelet artificial
Neural Network) was developed to explore the relationship between
climatic variables and sea level changes. In these models, wavelet
was used to disaggregate the time series of input and output data into
different components and then ANN was used to relate the
disaggregated components of predictors and input parameters to each
other. The results showed in the Shahid Rajae Station for scenario
A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea
level rise is among 90 t0 105 cm. Furthermore, the result showed a
significant increase of sea level at the study region under climate
change impacts, which should be incorporated in coastal areas
management.