Fuzzy Control Research Papers - Academia.edu (original) (raw)

In this work an automatic impedance matching for wireless communication systems is proposed. A Takagi-Sugeno (T-S) fuzzy logic controller is used to change adaptively the value of one capacitor in the π matching network. Asymptotic... more

In this work an automatic impedance matching for wireless communication systems is proposed. A Takagi-Sugeno (T-S) fuzzy logic controller is used to change adaptively the value of one capacitor in the π matching network. Asymptotic stability is established via a common Lyapunov function for all the subsystems of the T-S fuzzy system. Results obtained using Verilog-A and a Least Mean Square (LMS) impedance matching approach are presented.

Fuzzy Logic has been proposed for information representation and manipulation in databases, being the current tendency to study mechanisms of flexible querying to traditional databases. SQLf has been one of the efforts in this tendency.... more

Fuzzy Logic has been proposed for information representation and manipulation in databases, being the current tendency to study mechanisms of flexible querying to traditional databases. SQLf has been one of the efforts in this tendency. On the other hand, there are many different dialects of SQL. There are two major standards: ANSI (American National Standards Institute) SQL and an updated

This paper shows a strategy suitable for navigating autonomous robots in a completely unknown environment. The method proposed combines optimum path planning techniques with fuzzy logic to avoid obstacles and to determine the shortest... more

This paper shows a strategy suitable for navigating autonomous robots in a completely unknown environment. The method proposed combines optimum path planning techniques with fuzzy logic to avoid obstacles and to determine the shortest path towards its goal. The technique computes the potential surface using Dijkstra’s algorithm in a moving window, updating the cost map as it moves with the information obtained by the ultrasonic sensors. A Fuzzy Logic Controller (FLC) controls the wheels of a differential drive robot to the angle of minimum potential. This ensures a smooth trajectory towards the objective. A second FLC controls the average speed of the platform.

— This report proposes a design methodology for cascaded model-free fuzzy control systems. The ordinary Mamdani approach is modified in order to use expert knowledge for variable set-point control without any need of the system model. The... more

— This report proposes a design methodology for cascaded model-free fuzzy control systems. The ordinary Mamdani approach is modified in order to use expert knowledge for variable set-point control without any need of the system model. The methodology is successfully tested in a sub-actuated, naturally delayed setup, known as the coupled tanks system, where the water level is maintained at different set points, both in simulation and in real time.

Career guidance for students, particularly in rural areas is a challenging issue in India. In the present era of digitalization, there is a need of an automated system that can analyze a student for his/her capabilities, suggest a career... more

Career guidance for students, particularly in rural areas is a challenging issue in India. In the present era
of digitalization, there is a need of an automated system that can analyze a student for his/her capabilities,
suggest a career and provide related information. Keeping in mind the requirement, the present paper is an
effort in this direction. In this paper, a fuzzy based conceptual framework has been suggested. It has two
parts; in the first part a students will be analyzed for his/her capabilities and in the second part the
available courses, job aspects related to their capabilities will be suggested. To analyze a student, marks
in various subject in 10+2 standards and vocational interest in different fields have been considered and
fuzzy sets have been formed. On example basis, fuzzy inference rules have been framed for analyzing the
abilities in engineering, medical and hospitality fields only. In second part, concept of composition of
relations has been used to suggest the related courses and jobs.

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are... more

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are contributed by temperature, gas flow, and pressure. Error compensation is a key problem for these control systems. In recent years, it has been proven in the literature that artificial neural network (ANN) is a reliable and low cost solution to manage errors. Among all the algorithms of ANN, the back propagation is commonly used because of its simplicity and learning methodology is easy to realize. However, it has two notable drawbacks: (a) it is likely to run into local minimum, and (b) convergence is slow. Thermal conductivity gas analyzer often works in adverse surroundings, which requires fast and accurate measurements. Therefore, a strong learning network is needed. This paper proposes a novel thermal gas analyzer using adaptive neuro-fuzzy inference system. The effectiveness and validity of the proposed method is verified by simulation studies using MATLAB. Fuzzy membership rules are created to allow regulation of learning parameters. Further, the fuzzy adaptive network model is constructed to train large data samples while the high precision compensation of sensor error is realized by the improved flow. Simulation results reveal that the convergence speed and output accuracy is improved and the learning parameters in thermal gas analyzer are automatically corrected by the proposed method in comparison with the back propagation algorithm of artificial neural network.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves... more

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

This study involves the fuzzy logic controlling of brushless DC (BLDC) motor with rapid control prototyping method. Initially a MATLAB/Simulink model of the six pulse BLDC motor and its power electronic driver is established. A fuzzy... more

This study involves the fuzzy logic controlling of brushless DC (BLDC) motor with rapid control prototyping method. Initially a MATLAB/Simulink model of the six pulse BLDC motor and its power electronic driver is established. A fuzzy controller is developed by using MATLAB Fuzzy-Logic Toolbox and then it is inserted into the Simulink model. This model is directly communicated with a DSP through DS2201 dSPACE digital signal kit. PWM pulses are produced on the simulation model and DSP C program is directly developed through dSPACE system. It is shown that, fuzzy controller is successfully controlling the motor and, the model based programming of DSPs is very simple and versatile comparing with that of conventional method.

... Research (CECSTR) PO Box 4078,Dept. of Electrical Engineering, Prairie View,TX, 77446 moghheli~,ee.tamu.edu KN Toosi University of Technology, Mechanical Engineering Department, Tehran, Iran, kazemi@kntu.ac:ir ** *** ...

Linguistic Fuzzy Logic Controller (LFLC) 2000 is a complex tool for the design of linguistic descriptions and fuzzy control based on these descriptions. Unique methodology and theoretical results upon which is LFLC 2000 based are... more

Linguistic Fuzzy Logic Controller (LFLC) 2000 is a complex tool for the design of linguistic descriptions and fuzzy control based on these descriptions. Unique methodology and theoretical results upon which is LFLC 2000 based are presented. Then, main purposes of it are sketched and some implementation aspects are discussed. Presentation of existing and perspective applications concludes the paper.

Unmanned underwater vehicles (UUVs) have become an integral part in helping humans do underwater explorations more efficiently and safely since these vehicles can stay underwater much longer than any human can possibly do and they require... more

Unmanned underwater vehicles (UUVs) have become an integral part in helping humans do underwater explorations more efficiently and safely since these vehicles can stay underwater much longer than any human can possibly do and they require little or almost no human interaction. These vehicles are subject to dynamic and unpredictable nature of the underwater environment resulting to complexities in their navigation. This paper proposes a fuzzy logic-based controller to allow the vehicle to navigate autonomously while avoiding obstacles. The said controller is implemented in an actual low-cost underwater vehicle equipped with magnetometer and ultrasonic sensors. The intelligence of the UUV includes a two fuzzy logic block, namely Motion Control block and Heading Correction block. The fuzzy logic controller takes in target positions in X, Y and Z axes. Also, the heading error and rate of heading error are included as inputs in order to correct the bearing or direction of the vehicle. A ...

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an... more

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity complicates the system dynamics and makes the stability analysis difficult compared with the pure continuous-time fuzzy control systems. Moreover, the favourable property of the continuous-time fuzzy control systems which is able to relax the stability analysis result vanishes in the sampled-data fuzzy control systems. A Lyapunov-based approach is employed to derive the LMI-based stability conditions to guarantee the system stability. To facilitate the stability analysis, a switching fuzzy model consisting of some local fuzzy models is employed to represent the nonlinear plant to be controlled. The comparatively less strong nonlinearity of each local fuzzy model eases the satisfaction of the stability conditions. Furthermore, membership functions of both fuzzy model and sampled-data fuzzy controller are considered to alleviate the conservativeness of the stability analysis result. A simulation example is given to illustrate the merits of the proposed approach.

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy... more

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy inference systems (FISs) are introduced in this article. The IT2FFISs, which are outlined in this article, add to the armamentarium of FISs some particular concepts such as interval type-2 fractional membership functions, type-2 fractional translation rule, type-2 fracture index, the concept of switching, the entanglement, the degeneracy concept, and so forth. An IT2FFIS exploits not only the tolerance for the uncertainty in the interpretation of the meaning of a word, but also the relevance between the quality and quantity levels of the given information to infer an answer to an inference query. The IT2FFISs make an increase in machine intelligence quotient possible by an increase in the range of FISs order rather than their type. Moreover, the synergy of the concepts coming with various modes of IT2FFISs such as the aggressive mode opens a gate to a space of fuzzy systems outputs which used to be indiscoverable. Furthermore, it is demonstrated that as the type-2 fracture index approaches zero, the space of IT2FFISs outputs contracts and eventually it coincides the space of IT2FISs output when the fracture index is equal to zero. It is also proved that, provided a particular order of the IT2FFIS is taken into account, independent of the problem in question, a typical IT2FIS never leads to results which are more satisfactory than those obtained by the IT2FFIS corresponding to the typical IT2FIS.

This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tool. JPSN's ability to predict future trends of temperature was tested and compared to that of... more

This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tool. JPSN's ability to predict future trends of temperature was tested and compared to that of Multilayer Perceptron (MLP) and the standard Pi-Sigma Neural Network (PSNN); trained with the standard gradient descent algorithm. A set of historical temperature measurement for five years from Malaysian Meteorological Department was used as input data to train the networks for the next-day ...

several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper,... more

several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neural network and a model reference adaptive controller are used to estimate the inverse dynamic of this mechanism.
An online training algorithm based on the error dynamics is used on the adaptive neural network controller but the neural networks are trained offline with a backpropagation algorithm. The design and architecture of the neural networks are explained along with the identification procedure of the robotic system. Simulations and comparisons with a PD controller are done to test the performance of the neural network controller.

ABSTRACT Application of a fuzzy logic controller to a class of hydraulically actuated industrial robots is investigated. A simple set of membership functions and rules are described which meets certain control requirements. An off-line... more

ABSTRACT Application of a fuzzy logic controller to a class of hydraulically actuated industrial robots is investigated. A simple set of membership functions and rules are described which meets certain control requirements. An off-line routine based on the simplex method is outlined to tune the controller gains for an optimum response. The fuzzy control gains are tuned by minimizing the summation of absolute position errors over step input responses. The fuzzy logic controller is first examined through simulation of a two-link hydraulic robot of the same hydraulic configuration as many industrial manipulators. The controller has positive aspects which cannot be easily achieved by conventional control techniques. These include a fast rise-time and a well maintained damped response. The fuzzy controller is applied to an instrumented Unimate MK-II industrial hydraulic robot. The experimental results are encouraging in that the best performing control gains are found for different links with a reasonable number of trials and produce step responses with fast response and few oscillations at the set point. The controller demonstrates robustness

Refer to this research, a linear error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s parameters.... more

Refer to this research, a linear error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s parameters. Sliding mode controller has difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode controller and adaption law which this method can helps improve the system’s tracking performance by online tuning method. Since the sliding surface gain ( ) is adjusted by new linear tuning method, it is continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor which is a coefficient varies between half to one. Linear error-based tuning sliding mode controller is stable model-based controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in linear error-based tuning sliding mode controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.8e-10 and RMS error=1.16e-12).

The proper system for evaluating the learning achievement of students is the key to realizing the purpose of education. In recent years, several methods have been presented for applying the fuzzy set theory in the educational grading... more

The proper system for evaluating the learning achievement of students is the key to realizing the purpose
of education. In recent years, several methods have been presented for applying the fuzzy set theory in
the educational grading systems. In this paper, we propose a method for the evaluation of students’
answerscripts using a fuzzy system. The proposed system applies fuzzification, fuzzy inference, and
defuzzification in considering the difficulty, the importance and the complexity of questions. The transparency,
objectivity, and easy implementation of the proposed fuzzy system provide a useful way to
automatically evaluate students’ achievement in a more reasonable and fairer manner.
 2008 Elsevier Ltd. All rights reserved.

The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory... more

The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. The basic properties and examples of dual numerical means are considered.

Two approaches for constructing control charts to monitor multivariate attribute processes when data is presented in linguistic form are suggested. Two monitoring statistics T \(^{\rm 2}_{f}\) and W 2 are developed based on fuzzy and... more

Two approaches for constructing control charts to monitor multivariate attribute processes when data is presented in linguistic form are suggested. Two monitoring statistics T \(^{\rm 2}_{f}\) and W 2 are developed based on fuzzy and probability theories. The first is similar to the Hotteling’s T 2 statistic and is based on representative values of fuzzy sets. The W 2 statistic, being a linear combination of dependent chi-square variables, its distribution is derived by Satterthwaite’s approximation. Resulting multivariate control charts are compared based on the average run length (ARL). A numerical example is given to illustrate the application of the proposed multivariate control charts and the interpretation of out-of-control signals.

The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller with application to spherical motor is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to... more

The minimum rule base Proportional Integral Derivative (PID) Fuzzy Computed Torque Controller with application to spherical motor is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules and have lots of problem to design embedded control system e.g., Field Programmable Gate Array (FPGA). In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a conventional PI controller to have the minimum rule base and acceptable trajectory follow disturbance to control of spherical motor. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters for each direction of three degree of freedom spherical motor, this controller is work based on motor dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear spherical motor‟s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of three degrees of freedom spherical motor system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.
Index Term—PID

In this paper, an optimal PID-FLC (Proportional Integral Derivative Fuzzy Logic Controller) is proposed. The design of this system aims to control blood glucose elevation in type 1 diabetic patients. An automated system integrated with a... more

In this paper, an optimal PID-FLC (Proportional Integral Derivative Fuzzy Logic Controller) is proposed. The design of this system aims to control blood glucose elevation in type 1 diabetic patients. An automated system integrated with a miniaturized insulin infusion pump and a continuous biosensor that measures the glucose level has been developed recently to replace beta cells in the pancreas. The main contribution of the paper is that it introduces an automated insulin delivery system based on a parallel PID-FLC structure tuned with genetic algorithms. This control system was compared to an optimal PIFLC and PD-FLC as well as a reference model. The results revealed that the controllers could maintain the glucose level within a normal range. In addition, the performance of the PIFLC and the PID-FLC was very close to that of beta cells in normal individuals. So, they can be exploited prosperously as control systems to manage blood glucose concentrations in Type 1 diabetic patients. In addition, the PID-FLC saved the amount of the daily delivered insulin, while, its performance was approximately the same as that of the PI-FLC.