I N the last three decades process control and automation area had a tremendous improvement due to advances on computational tools. Many of regulatory control actions that were performed by human operators are now performed automatically with aid of computers. Nonetheless, in a pro- cess with hundreds of variables, instruments and actuators it is impossible that a person or a group can manage every and any alarm triggered by an abnormal event. Therefore the Fault Detection and Diagnosis (FDD) field had received extensive attention. According to , the current challenge for control engineers is the automation of Abnormal Event Management (AEM) using intelligent control systems. Inside this field, Instrument Fault Detection and Diagnosis is a potential tool to prevent process performance degradation, false alarms, missing actions, process shutdown and even safety problems. A well-known strategy related to this pro- blem is preventive maintenance. In that, periodical tests and calibration are made in instruments. This is a cumbersome task where instruments are dismantled, cleaned, reassembled and calibrated. Even so, this is not a guarantee that faults will not occur . This paper presents an IntervalType- 2FuzzyLogic (IT2FL) classifier to detect and diagnose temperature sensor faults in an alternative compressor, named Sales Gas Compressor (SGC), operating in a Gas Processing Unit (GPU).
Abstract—Robotics control system with leader-follower approach has a weakness in the case of formation failure if the leader robot fails. To overcome such problem, this paper proposes the formation control using IntervalType-2- FuzzyLogic controller (IT2FLC). To validate the performance of the controller, simulations were performed with various environmental systems such as open spaces, complexes, circles and ovals with several parameters. The performance of IT2FLC will be compared with Type-1 FuzzyLogic (T1FL) and Proportional Integral and Derivative (PID) controller. As the results found using IT2FLC has advantages in environmental uncertainty, sensor imprecision and inaccurate ac- tuator. Moreover, IT2FLC produce good performance compared to T1FLC and PID controller in the above environments, in terms of small data generated in the fuzzy process, the rapid response of the leader robot to avoid collisions and stable movements of the follower robot to follow the leader's posture to reach the target without a crash. Especially in some situations when a leader robot crashes or stops due to hardware failure, the follower robot still continue move to the target without a collision.
Abstrak Makalah ini menyajikan sebuah aplikasi IntervalType-2FuzzyLogic System (IntervalType-2 FLS) untuk peramalan beban jangka pendek (STLF) pada hari-hari libur dalam sebuah studi kasus di Bali, Indonesia. Sistem logika fuzzyType-2 ditandai dengan konsep yang disebut footprint of uncertainty (FOU). Penggunaan dimensi matematika tambahan pada sistem Type-2 FLS ini memberikan hasil yang lebih baik dibandingkan dengan Type-1 FLS. Type-2 FLS juga memiliki kemam- puan untuk memodelkan hubungan yang lebih kompleks dengan output dari Type-2fuzzy inference engine yaitu memerlukan apa yang disebut dengan type reduction. Type reduction yang digunakan ini menerapkan algoritma iteratif Karnik-Mendel (KM Algorithm) yaitu pemetaan output Type-2fuzzy set (Type-2 FSs) menjadi Type-1 fuzzy set (Type-1 FSs) dengan proses defuzzi- fikasi menggunakan centroid untuk selanjutnya mengkonversi Type-1 FSs ke suatu bilangan. Metode yang diusulkan ini diuji dengan data beban puncak hari libur sebenarnya pada sistem kelistrikan Bali, yaitu hari libur menggunakan beban puncak 4 hari sebelum hari libur dan pada saat hari libur untuk periode tahun 2002 hingga tahun 2006. Ada 20 item hari libur di Bali yang digunakan untuk peramalan pada tahun 2005 dan 2006. Hasil tes menunjukkan tingkat keakuratan peramalan dengan persentase kesalahan rata-rata (MAPE) sebesar 1,0335% di tahun 2005 dan 1,5683% di tahun 2006.
In this study, a novel variable impedance control for a lower-limb rehabilitation robotic system using voltage control strategy is presented. The majority of existing control approaches are based on control torque strategy, which require the knowledge of robot dynamics as well as dynamic of patients. This requires the controller to overcome complex problems such as uncertainties and nonlinearities involved in the dynamic of the system, robot and patients. On the other hand, how impedance parameters must be selected is a serious question in control system design for rehabilitation robots. To resolve these problems this paper, presents a variable impedance control based on the voltage control strategy. In contrast to the usual current-based (torque mode) the use of motor dynamics lees to a computationally faster and more realistic voltage-base controller. The most important advantage of the proposed control strategy is that the nonlinear dynamic of rehabilitation robot is handled as an external load, hence the control law is free from robot dynamic and the impedance controller is computationally simpler, faster and more robust with negligible tracking error. Moreover, variable impedance parameters based on IntervalType-2FuzzyLogic (IT2Fl) is proposed to evaluate impedance parameters. The proposed control is verified by a stability analysis. To illustrate the effectiveness of the control approach, a 1-DOF lower-limb rehabilitation robot is designed. Voltage- based impedance control are simulated through a therapeutic exercise consist of Isometric and Isotonic exercises. Simulation results show that the proposed voltage- based variable impedance control is superior to voltage-based impedance control in therapeutic exercises.
The contribution of this paper is a novel empirical method for constructing an IntervalType-2FuzzyLogic System (IT2FLS) for automated cyber security assessments. The method extends a scalable technique for acquiring adequacy ratings of security requirements by measuring the extent to which these requirements interact to affect security, while accounting for uncertainty across raters . The use of fuzzy sets (FSs) associated with linguistic labels, in combination with fuzzylogic rules provides the resulting decision support system with a high degree of human (expert) interpretability, which in turn is vital for its evaluation and acceptance. The paper specifically adopts Type-2 FSs which offer an advantage over Type-1 FSs, because they allow the distinct capture of both inter- and intra-expert uncertainty . In this paper, we present a series of studies to construct and evaluate the IT2FLS in using a series of scenarios.
Liang and Mendel  introduced the theory and design procedures of IntervalType-2FuzzyLogic Systems (IT2 FLS). The IT2 FLS has always been considered to be a special case of a general T2 FLS; consequently, things that were developed for the latter were then specialized to the former. Research works about developing IT2 FLS were presented since Mendel et al.  have proposed a simple way to implement an IT2 FLS from T1 FLS mathematics. The use of IT2 FLS began to increase since then. Some papers  –  investigated the importance of IT2 FLS theoretically and practically, especially in intelligent control systems.
During the last two decades, the Internet has changed people’s habits and improved their daily life activities and services. In particular, the emergence of e-commerce provided manufac- tures and vendors with more business opportunities. This allowed customers to beneﬁt from a global, quicker and cheaper shopping environment. However, e-commerce is evolving from a centralised approach, where consumers directly purchase products and services from busi- nesses, to a Peer-to-Peer (P2P) perspective, in which customers buy and sell goods amongst themselves. In P2P scenarios, it is crucial to protect both buyers and sellers (the peers) from being victimised by possible fraud arising from the uncertainties, vagueness and ambiguities that characterise the interactions amongst unknown business entities. For this reason, the so-called reputation models are becoming a key architectural component of any e-commerce portal. These systems are intended to evaluate the basic features of each entity (buyer, seller, goods, etc.) involved in a given trading transaction in order to assess the trust level of the given transaction and minimise fraud. However, in spite of their wide deployment, the rep- utation models need to be enhanced to handle the various sources of uncertainties in order to produce more accurate outputs which will allow to increase the trust and decrease the fraud levels within e-commerce systems. In this paper, we present an intervaltype-2fuzzylogic based framework for reputation management in (P2P) e-commerce which is capable of better handling the faced uncertainties. We have carried out various experiments based on eBay ® -like transaction datasets which have shown that the proposed type-2fuzzylogic based
feature extractor and a classifier based on intervaltype-2fuzzylogic system. The proposed method can reduce the quantity of extracted features of distorted signal without losing its characteristics and thus, re- quires less memory space and computation time. The performance of classifier is test under three conditions i.e. ideal, SNR 30 dB and SNR 20 for ef- fective classification of PQ events. It is observed that IT2FLS correctly classifies the PQ event with high accuracy and IT2FLS gives the best performance as compared to neural network based classifier and SVM . Therefore, the proposed method can be used as the PQ event classifier in real time system. The overall classification efficiency of IT2FLS is 93% if take average of all three condition mention in this paper. The simulations result show that IT2FLS has higher performance than ANN with feed forward multilayer back propagation (FFML), learning vector quantization (LVQ), probabilistic neural network (PNN) [13, 14].
The use of convectional automatic voltage Regulator (CAVR) in synchronous generators to control the terminal voltage and reactive power has been the common phenomena in power systems control. Synchronous generators are nonlinear systems which are continuously subjected to load variations and the AVR design must cope with both normal load and fault condition of operation. Evidently, these conditions of operation result to considerable changes in the system dynamics. When the CAVR with fixed gain are used, the performance worsens and in some cases, introduces negative damping and degraded system stability. So far, a lot of work has been done in synchronous machine excitation stabilization using CAVR and controllers, all geared toward overcoming the problems enumerated above. The short comings here is that the parameters of the controllers are fixed and so if the system dynamics changes as a result of faults, the controller will be tuned manually to adjust. Modern control techniques are used extensively to achieve self-tuning (ST) control in synchronous generators. These include minimum variance (MV), generalized minimum variance (GMV), optimal predictor and pole placement (PP). In all these ST-AVR work, additional signals are used to improve robustness and are generally nonlinear. The MV generally gives very lively control and can be highly sensitive to non minimum phase plant. GMV, which is more robust and generalized, is vulnerable to unknown or varying plant dead time and can have difficulty with d.c offsets. PP aims to locate the closed-loop poles of the system at pre-specified locations leading to smooth controllers, but the algorithm shows numerical sensitivity when the plant model is over parameterized. Of recent, a lot of research is going on in areas of application of soft computing (fuzzy and neural approach) in synchronous generator controls. This work is based on intervaltype- 2fuzzylogic controller (IT2FLC). (IT2FLC) in synchronous generator (SG) terminal voltage and reactive power control is designed so that it has the ability to improve the performance of intervaltype-2fuzzylogic controller. The intervaltype-2fuzzylogic controller is superior to conventional AVR controllers which continue to tune the controller parameters because it will tune and to some extent remember the values that it had tuned in the past.
The model predicts an increase in the melt temperature when the barrel temperature is set to a higher value. The higher barrel temperature results in more heat energy being transferred from the barrel wall to melt the solid polymer. Consequently, the melting mechanism commences earlier and the solid is completely melted sooner than the case where lower barrel temperature is applied. The model has been tuned automatically to approximate the operational- sensitive parameters in the new operating environment. The model is further evaluated when it is employed as the predictive model in the control Scheme namely as the intervaltype-2fuzzylogic controller (IT2FLC). Fig.13indicates that without the adaptation, both of the plant outputs at die diverge if the prediction error exists. When the model adaptation commences, specifically when the approximations of the models have been tuned automatically according to the varying operating conditions, the melt temperature is regulated within tolerances as shown in Fig.14,respectively.
Many researches dealt with the problem of induction motors fault detection and diagnosis. The major difficulty is the lack of an accurate model that describes a fault motor. An intervaltype-2fuzzylogic approach may help to diagnose induction motor faults. The motor condition is described using linguistic variables. An intervaltype-2fuzzylogic system (IT2FLS), which can handle rule uncertainties. The implementation of this intervaltype-2fuzzylogic (IT2FLS) involves the operations of fuzzification, inference, and output processing. We focus on “output processing,” which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number). Intervaltype-2fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the intervaltype-2fuzzy inference. The induction motor condition is diagnosed using a compositional rule of intervaltype-2fuzzy inference. This paper presents a use of intervaltype-2fuzzylogic technique to diagnose stator fault by sensing stator currents and voltage.
In recent research, the condition monitoring and fault detection of electrical motors have moved to AI techniques, including neural networks and fuzzylogic, from traditional methods, because no detailed analysis of the fault mechanism is necessary and no modeling is required. Pereira and Silva studied the practical implementation of a system for the detection and diagnosis of broken rotor bars in electrical induction motors. Goddu et al. (1998) analyzed bearing vibration signals using fuzzylogic fault diagnosis methodology. Their study results showed that fuzzylogic can be used for accurate bearing fault diagnosis if the input data are processed in an advantageous way. In this proposed work intervaltype-2fuzzylogic is proposed along with MCSA for fault diagnosis of induction motor, because it can better deal with the uncertainty in the signals used for processing.
In mobile robotics, some researchers have explored the control of mobile robots using intervaltype-2fuzzylogic [21,24-29]. As Hagras states in  control using type-2fuzzy sets represents a new generation of fuzzy control- lers. In  Hagras presented an intervaltype-2fuzzylogic controller to command a robot in indoor and out- door unstructured environment. A robot was tested under different sources of non-systematic errors. The results showed that type-2fuzzylogic outperforms its type-1 counterpart. This was shown through robot paths and control surfaces. In , an intervaltype-2fuzzylogic was proposed for the control of a robot tracking a mobile object in the context of robot soccer games. In this game the robot has to track a ball. To evaluate the performance of the type-2fuzzylogic against its type-1 counterpart, graphical paths analysis were presented showing the way the player reaches the position of the ball. Also, an addi- tional test was made presenting the ability of type-2 con- troller to track the ball with less standard deviation error than its type-1 counterpart.
ABSTRACT: This work aims to develop a controller based on evolutionary intervaltype-2fuzzylogic to simulate an automatic voltage regulator (AVR) in transient stability power system analysis. It was simulated a one machine control to check if the evolutionary intervaltype-2fuzzylogic controller (EIT2FLC) implementation was possible. After which results were compared to the results obtained with the AVR itself. The traditional type-1 FuzzyLogic Controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2fuzzy sets can handle such uncertainties to produce a better performance. However, manually designing the type-2 Membership Functions (MFs) for an intervaltype-2 FLC is a difficult task. This paper will present Genetic Algorithm (GA) based architecture to evolve the type-2 MFs of intervaltype-2 FLCs used for transient stability power system analysis. The GA based system converges after a small number of iterations to intervaltype-2 MFs which gave very good performance. The control of synchronous generator terminal voltage and reactive power has been a disturbing problem to researchers and designers of power system engineers. This paper uses the evolutionary intervaltype-2fuzzylogic scheme for automatic voltage regulation of synchronous generators and observes that it is superior to convectional AVR controllers because it can tune the controller parameters and remembers what it tuned before. The simulation results obtained using evolutionary intervaltype-2fuzzylogic scheme during abnormal operation of power system network shows reduction in settling time, percentage overshoot and steady state error.
Abstract— BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2fuzzylogic as classifier, an automatically generated rules will be applied to the model. To evaluate the proposed model, accuracy, specificity and sensitivity of the modal will be calculated and compared vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department, Hospital of National University of Malaysia (UKM). The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2fuzzylogic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert rules achieve 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.
is a logical conclusion that can be made as a greater vertical movement is more difficult to complete. Secondly we can observe that in the case of Relative Performance every line is very close together indicating that in fact the FOU does not seem to affect the relative performance as much as the Uncertainty present in the environment in these experiments. Tables III IV V show the three input variables and resulting Relative Performance values for a single course in which 50m of vertical movement is required to complete. Each Table enumerates the values for different FOU sizes, 0, 10 and 20 respectively and it can be observed that the uncer- tainty values remain relatively constant, an expected result as in each experiment the wind conditions are repeated. The fact that the values do not change in the different tables give further evidence that FOU does not in fact affect the performance of type-2 controllers in these experiments.
As previously stated, GIT2FS is basically a fuzzy system augmented by a learning process based on a genetic algorithm (GA). In GIT2FS, GAs operates to search an appropriate Knowledge Base (KB) of a fuzzy system for a particular problem and to make sure those parameter values that are optimal with respect to the design criteria. The KB parameters constitute the optimization space, which is transformed into suitable genetic representation on which the search process operates. The KB is composed by intervaltype-2 membership functions (IT2MF), shortly (MF), and fuzzy rule base (RB), as mentioned before. So, there are some options to design Genetic IT2 Fuzzy System, e.g. tuning or learning membership functions, or fuzzy rule base or both of them, sequentially or concurrently. When tuning membership functions, an individual population represents parameters of the membership function shapes at which fuzzy rule base is predefined in advance. In contrast, if be desired to tune fuzzy rules base, the population represents all of fuzzy rules possibility that membership functions is assumed before. Fig.1 shows these conceptions.
According to The the fuzzy control theory, which was presented by Zadeh on the theory of fuzzy systems, has attracted the attention of many researchers in controlling structures . The remarkable features of this method have been greatly appreciated. This method solves the need for precise mathematical modeling of the structure by applying a series of innovational rules. Other features of this control algorithm can be its robustness against the uncertainties and errors in the various parts of the control system such as data, loads, structure model, measurements, etc. Another important feature of this method is the ability to use it in non-linear systems. Due to the nature of non-linear behavior of structures, this method can be used to control structures. Using human knowledge and experience in controller design and the possibility of adapting the control system can be considered as the other advantages of this method than in comparison with other control methods. In this paper, the type-2fuzzy systems, which are in fact a development of type-1 fuzzy systems, are applied. In the following, the equations and components of the type-2fuzzy system are briefly described. The type-2fuzzy set is represented by Eq. (2) and Eq. (3)uations 2 and 3.
ABSTRACT: In this paper an Interval Type2 FuzzyLogic (IT2FL) controller is proposed for the control of DC-DC converters to attain a good output voltage regulation and dynamic response. The buck and boost type DC-DC converters are considered for the implementation of the IT2FL controller. To study the effects on the system performance, the conventional PI and type-1 fuzzy controller are designed and compared with intervaltype-2fuzzy controller. Design of PI control is based on the frequency response of the DC-DC converter. Design of IT2FL controller is based on heuristic knowledge of converter behavior and tuning requires some expertise to minimize unproductive trail and errors. The setting time, the overshoot and the steady state error of the converter are used as the performance criteria for the evaluation of the controller performance. From the comparison, it is inferred that IT2FL controller will give better result than other controllers.
Where I is the total number of start position, K is the number of step simulation for each start position, ω(k), and v(k) are the rotational speed and the faulty speed at k, respectively, and c is constant for health check of IM, 0 if there is no fault and 1 if there is fault. This function is minimized in order to achieve the condition than the motor run by avoiding fault, higher speed, and mostly reliable speed. Recombination, mutation and crossover, are three operators of GA with fixed crossover probability rate (Pc) and probability mutation rate (Pm), that are 0.7, and 0.7 parameter numbers, respectively. The number of new generation is adjusted by Generation Gap constant (GGAP), which is 0.9. The procedure is repeated until the termination condition is reached. It has been presented Interval Type2Fuzzy Logic Controller (IT2FLC) where the fuzzy knowledge based, i.e. membership functions and rule bases, are tuned by Genetic Algorithm (GAs), known as Genetic Fuzzy System (GIT2FS), to generate individual command action. The model is designed in order to detect faults in IM. The best fitness knowledge base is obtained by learning the RB in advance and then tuning the MF after. B