Impedance control is an effective method in controlling the rehabilitation robots. The therapeutic exercises are performed well with satisfactory performances. The previous impedance control approaches were developed based on the torque control strategy whereas the proposed impedance control is based on the voltage control strategy. The proposed approach is free of manipulator dynamics, thus is simpler, less computational, and more effective compared with the torque based control approaches. Additionally, since it is difficult to be sure about the ideal value of the impedance parameters, interval type-2fuzzylogic systems are used to regulate impedance parameters. The proposed technique for applying the adaptive impedance parameters is shown to be more efficient than using the constant impedance parameters. The control approach has been verified by stability analysis. The simulation results show the superiority of the proposed control approach over a constant impedance control scheme.
The paper highlighted some of the problems associated with the polymer extrusion modeling for real time control applications. A dynamic model has been developed to estimate the melt temperature and the pressure at the die. These parameters are regarded as the quality indicators of the extrusion operation. Interval type2 fuzzy rule-based (IT2FRB) model is applied as a means to capture the non-linearity characteristics of the operational-sensitive parameters. An interval type-2fuzzylogic (IT2FLC) control framework to achieve the desired average die melt temperature was proposed and the simulation results confirmed its efficacy. The controller determines the average melt temperature based on a radial temperature profile of the die melt flow rather than a point-based measurement which is less accurate although common in practice. It is shown that interval type2 fuzzy rules based controller (IT2FRBC) provide good control capabilities to maintain the melt temperature homogeneity within desired limits by manipulating screw speed and barrel set temperatures in parallel. The controller performances can be further improved by improving the models accuracies, adding more fuzzy rules etc. Therefore, this may offer a new method to operate extruders at high screw speeds whilst achieving both high energy and thermal efficiencies.
Estimating the effort required for software production and development has been studied for many years and numerous articles have so far been published in this area. Recent statistics suggest the fact that work done in this area does not meet the needs of software developers. In this paper, we have tried to present an efficient model for accuracy improvement and software effort estimation compatibility. The proposed model is based on using type-2fuzzylogic. In order to teach type-2fuzzylogic, the gradient descent method and the neuro-fuzzy-genetic hybrid approach have been used. In addition, for assessing the accuracy of the proposed model, three of the most widely used assessment parameters, relative error median, relative error mean, and prediction
Liang and Mendel  introduced the theory and design procedures of Interval Type-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.
Data-driven computational intelligence (DDCI) models have gathered much pace and popularity due to the rapid growth in computing power and the availability of extensive data and information in modern industrial processes. Common DDCI paradigms that are often employed to describe complex processes and solve engineering problems include, but not limited to, neural networks (NN) (Bishop, 2006), fuzzy rule- based systems (FRBS) (Jang and Sun, 1996), and evolutionary and genetic algorithms (GAs) (Yang et al., 2003). In contrast to other DDCI methodologies, fuzzy rule- based systems offer good level of transparency and simplicity in their structure. Recently, studies on type-2fuzzylogic systems (FLSs) have attracted much attention (Bustince Sola et al., 2015; Mendel, 2015) due to their capacity to capture uncertainty in the input linguistic variables (an extra degree of freedom compared to Type-1 sets). In addition, Type-2 FLSs appear to be more promising compared to their type-1 counterparts in handling uncertainties such as those associated with noisy data and different word meanings. Thus, type-2fuzzy sets allow for better capture of uncertainties in rule-based systems.
First input sensors provide crisp inputs. Fuzzifier converts crisp input into type-2fuzzy sets. Fuzzifier is used for fuzzification. Fuzzification refers to replacing a crisp set into set with sharply defined boundaries, with a set whose boundaries are fuzzy. Generally there are three types of Fuzzifier; (i) singleton Fuzzifier, (ii) trapezoidal or triangular Fuzzifier and (iii) Gaussian Fuzzifier. Usually used singleton fuzzification in interval type-2 FLC due to simplicity and suitability. Type-2fuzzy is input of interface engine and it activates the interface engine. Rule base produce output type-2fuzzy sets. The type-2 FLC rules are same as in a type-1 FuzzyLogic Controller. But consequents or/and the antecedents will be represented by interval type-2fuzzy sets. Fired rule combines with the help of interface engine and produce mapping from input type-2fuzzy sets to output type-2fuzzy sets. Output of interface engine (output type-2fuzzy sets) is input of type-reducer which combine output sets and centroid calculation take place, which conduct to type-1 fuzzy sets called the type-reduced sets [Karnik, N.N. et. al, 1998]. Projection of a type-2fuzzy set into a type-1 fuzzy set and also acts as a measure of uncertainty for that set is possible due to centroid of type-2fuzzy set. Type- reduction in a type-2fuzzylogic system is an advanced version of the defuzzification operation in a type-1 FLS. There are many types of type-reduction methods such as height, modified height, center-of-sets and centroid [Qilian Liang et. al, 2000].
Obesity is one of the world's fastest evolving health challenges in the poorest and wealthiest countries. Childhood obesity is rising exponentially worldwide due to sev- eral factors. Family’s dirty habits patterns and characteristics play a significant role in causing the risk of obesity for their children. This paper reviewed the Artificial Intel- ligence techniques used out for the childhood obesity field. Besides, this paper pro- posed conceptual frameworks that aims to use novel type-2fuzzylogic methodology that are capable of predicting the risk of obesity for children based on their family’s dirty habits patterns, characteristics, and other parameters related to the home envi- ronment and the child itself. This prediction will be used as an intervention factor to remediate obesity, which will enhance public health and reduce the costs of later treatments for several obesity-related diseases. We are planning to evaluate the pro- posed conceptual methodology on at least 1,000 families and their children within Saudi Arabia. The proposed type-2fuzzylogic-based systems will be capable of han- dling the encountered uncertainties to achieve better modeling and a more accurate risk of obesity for children. They can also encode the extracted rules in comprehen- sive ways to provide insights for the best obesity prevention behaviors for getting obese child risk.
Abstract—Navigation is one of the typical problem domains occurred in studying swarm robot. This task needs a special ability in avoiding obstacles. This research presents the navigation techniques using type 1 fuzzylogic and interval type2fuzzylogic. A comparison of those two fuzzylogic performanc- es in controlling swarm robot as tools for complex problem modeling, especial- ly for path navigation is presented in this paper. Each hierarchical of fuzzy log- ic shows its advantages and disadvantages. For testing the robustness of type-1 fuzzylogic and interval type-2fuzzylogic algorithms, 3 robots for the real swarm robot experiment are used. Each is equipped with one compass sensor, three distance sensors, and one X-Bee communication module. The experi- mental results show that type-2fuzzylogic has better performance than type-1 fuzzylogic.
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 interval type-2fuzzylogic approach may help to diagnose induction motor faults. The motor condition is described using linguistic variables. An interval type-2fuzzylogic system (IT2FLS), which can handle rule uncertainties. The implementation of this interval type-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). Interval type-2fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the interval type-2fuzzy inference. The induction motor condition is diagnosed using a compositional rule of interval type-2fuzzy inference. This paper presents a use of interval type-2fuzzylogic technique to diagnose stator fault by sensing stator currents and voltage.
This paper discusses the new approach MRAS (IS- MRAS) and rotor resistance estimation for speed sensorless control induction motor with on-line adaptation parameters. A novel adaptation mechanism using type-2fuzzylogic controller, which replaces conventionally used PI controller in the adaptation mechanism. The speed estimation using the PI controller deteriorates at low speeds and hence proposed a T2FLC based speed observer that not only improves the performance during low speed but also makes the motor robust to external load torque disturbances. Simulation and experimental results show good
Type-1 fuzzylogic (T1FL) controllers have been successfully used in numerous applications many of which are too complex to be analyzed using conventional mathematical techniques for years. In design of T1FL controllers, the experience and knowledge of human experts are needed to determine parameters associated with the rule base and membership function . Type-1 Fuzzy Systems is also called as conventional fuzzy systems. Type-2 system is capable of handling uncertainties involved with type-1 fuzzy systems. Zadeh  introduced type-2 and higher-types fuzzy systems in 1975 to eliminate the paradox of T1FL systems which can be formulated as the problem that the membership grades are themselves precise real numbers. Type-2 and higher-types systems are an extension of T1FL systems. Type-1 fuzzy sets are not able to directly model such uncertainties because their membership functions are totally crisp. On the other hand, type-2fuzzy sets are able to model such uncertainties because their membership functions are themselves fuzzy. Membership functions of type-1 fuzzy sets are two-dimensional, whereas membership functions of type-2 sets are three-dimensional. The new third- dimension of type-2fuzzy sets that provides additional degrees of freedom that make it possible to directly model uncertainties. Similar to T1FL systems, Type-2FuzzyLogic (T2FL) systems comprise fuzzifier, rule base, inference engine and output processor.
C. Development of Proposed Type-2FuzzyLogic Model To classify BIRADS for breast cancer, type-2fuzzylogic had been selected. This fuzzy rule based system was also known as Mamdani. According to Caramihai et al., Fuzzylogic model can be developed based on three steps. Firstly, was the determination of input and output variables that described breast cancer tissue problem and choose interval for the variable. The second step was defining linguistic set values of breast cancer tissue along with its membership function that could be mapped onto fuzzy variable range and finally was constructing rule sets using radiologist rules and auto-generated rules using rough set in parallel by associating their input and output.
The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases have established their robust and that their performance is less sensitive to parameter variations over conventional controllers. In this paper, Interval Type-2FuzzyLogic Controller (IT2FLC) method is proposed for controlling the speed with a direct stator flux orientation control of doubly-fed induction motor (DFIM), we made a comparison between the Type-1 FuzzyLogic Control (T1FLC) and IT2FLC of the DFIM, first a modeling of DFIM is expressed in a (d-q) synchronous rotating frame. After the development and the synthesis of a stabilizing control laws design based on IT2FLC. We use this last approach to the control of the DFIM under different operating conditions such as load torque and in the presence of parameter variation. The obtained simulation results show the feasibility and the effectiveness of the suggested method.
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 interval type-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
This paper addresses the problem of the tracking of moving target by a mobile ro- bot. The proposed approach aims to design an intelligent controller based on fuzzylogic techniques. The principal role of arti…cial intelligence techniques is their ability to design robust controllers with good performances in spite of the lack of information about the mobile robot or its surrounding environment models. Two type-1 fuzzylogic controllers : attraction to a dynamic target and obstacle avoidance based on Mamdani model and have been adopted to determine the mobile robot’s velocities to ful…ll the control objec- tives. A type-2fuzzylogic controller has been also implemented for better e¢ ciency and e¤ectiveness.
Summary. In this chapter, we will present the novel applications of the Interval Type-2 (IT2) FuzzyLogic Controllers (FLCs) into the research area of computer games. In this context, we will handle two popular computer games called Flappy Bird and Lunar Lander. From a control engineering point of view, the game Flappy Bird can be seen as a classical obstacle avoidance while Lunar Lander as a position control problem. Both games inherent high level of uncertainties and randomness which are the main challenges of the game for a player. Thus, these two games can be seen as challenging testbeds for benchmarking IT2-FLCs as they provide dynamic and competitive elements that are similar to real- world control engineering problems. As the game player can be considered as the main controller in a feedback loop, we will construct an intelligent control systems composed of three main subsystems: reference generator, the main controller, and game dynamics. In this chapter, we will design and then employ an IT2-FLC as the main controller in a feedback loop such that to have a satisfactory game performance while be able to handle the various uncertainties of the games. In this context, we will briefly present the general structure and the design methods of two IT2-FLCs which are the Single Input and the Double Input IT2-FLCs. We will show that an IT2 fuzzy control structure is capable to handle the uncertainties caused by the nature of the games by presenting both simulations and real-time game results in comparison with its Type-1 and conventional counterparts. We believe that the presented design methodology and results will provide a bridge for a wider deployment of Type-2fuzzylogic in the area of the computer games.
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 Interval Type- 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 This paper presents the application of Interval Type-2fuzzylogic systems (Interval Type-2 FLS) in short term load forecasting (STLF) on special days, study case in Bali Indonesia. Type-2 FLS is characterized by a concept called footprint of uncertainty (FOU) that provides the extra mathematical dimension that equips Type-2 FLS with the potential to outperform their Type-1 counterparts. While a Type-2 FLS has the capability to model more complex relationships, the out- put of a Type-2fuzzy inference engine needs to be type-reduced. Type reduction is used by applying the Karnik-Mendel (KM) iterative algorithm. This type reduction maps the output of Type-2 FSs into Type-1 FSs then the defuzzification with centroid method converts that Type-1 reduced FSs into a number. The proposed method was tested with the actual load data of special days using 4 days peak load before special days and at the time of special day for the year 2002-2006. There are 20 items of special days in Bali that are used to be forecasted in the year 2005 and 2006 respectively. The test results showed an accurate forecasting with the mean average percentage error of 1.0335% and 1.5683% in the year 2005 and 2006 res- pectively.
The research reported here introduces a new method for learning general type- 2fuzzy systems with a unique combination of learning the footprint of uncertainty (FOU) followed by learning the secondary membership functions (SMF). In addition, we show that when using the vertical slice type reducer we have improvement over other approaches implemented here. Furthermore, interval type-2fuzzylogic systems were applied to answer the question of to what extent general type-2fuzzy sets can add more abilities and flexibilities to modeling than interval type-2fuzzy sets. A detailed analysis is carried out of the learning of general type-2fuzzy systems on a set of real- world data with and without added noise and, as such, provides significant insight into how the future of learning general type-2fuzzy systems can be carried out. These methods are applied to four benchmark problems: noise-free Mackey-Glass time series forecasting , noisy Mackey-Glass time series forecasting , and two real-world problems, namely the estimation of the low-voltage electrical line length in rural towns and the estimation of the medium-voltage electrical line maintenance cost .
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 Interval Type-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.