Fuzzy Logic Controller

This paper preludes the fuzzy logic algorithms as artificial intelligence (AI) inference engine. That might be the development and implementation of highly superior AI computing machines. For that purpose of the AI model, many fuzzy concepts have been proposed; fuzzy connections (if-then, union and intersection), fuzzy meta-implication, fuzzy inference, fuzzy meta-language and fuzzy predicate. Accordingly, the fuzzy AI model is efficiently implemented in a fuzzy logic controller as a fuzzy algorithm. This algorithm methodology is based on the linguistic approximation to implement the semantic as control commands.

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.

In this study, an educational tool has been presented to teach fuzzy logic controller (FLC) and classical controllers to undergraduate and graduate level students, so that students could establish a thorough understanding and be able to compare FLC with classical controllers. The tool has flexible structure and user-friendly graphical interface. Direct current (DC) motor speed control has been presented to help students learn and compare the FLC and classical controllers. Students practice on both controller, interpret and draw conclusions related to system parameters by changing parameters of FLC and classical controller with various working conditions. ß

The aim of this paper is to present a new fuzzy control scheme for real time drive efficiency improvement by the adaptive flux reduction at light loads during steady states and by the optimum flux recovery during transient states. The optimum flux recovery instead of the rated flux does not sacrifice the drive dynamic response since the torque capacity remains the same and accelerates the search convergence in steady states. The control scheme is cooperated with an Indirect-Field-Oriented-Control (IFOC) motor drive. The design of the new fuzzy control scheme is focused to recognize the motor state operation in order to control under the corresponding circumstances. The proposed fuzzy control scheme includes three main units: one Fuzzy-Logic-Search Controller FLSC1, one Fuzzy-Logic-Controller FLC2 and a State detector unit. The State detector unit decides which of the FLSC1 or FLC2 controllers is used, in accordance to the operation circumstances. The fuzzy controllers utilize the stator flux-producing current component into d-q reference frame (stator current direct axis i sd) as control variable, in order to estimate the next optimal change of Δi sd. The drive efficiency improvement and the drive dynamic response is currently validated in laboratory using an experimental array of a 3phase 1 Hp induction motor drive. The control scheme is implemented using the platform eZdsp from Digital Spectrum based on the fixed point 32 bit DSP TMS320F2812 from TI.

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.

Ahstract-In this paper, a software simulation model is developed for a two wheels driven mobile robot motion controller that can navigate the robot safely through an unknown environment. The work involves the design of a controller, which has four functions: motion control; obstacle avoidance; self-location; and path planning both global and local. The proposed controller is responsible for the mobile robot navigation after it generates a trajectory between start and goal points. Also it enables the robot to operate successfully in the presence of various obstacles present in any user built maps. The mobile robot is able to locate its position on any given map. The dynamic of the mobile robot is examined and the time constant of the two motors, which affects the direction of the mobile robot motion, is controlled. Obstacle avoidance is implemented with Fuzzy Logic Controller. The numerical experiments demonstrated that the indoor robot navigated successfully in tight corridors, avoided obstacles and dealt with a variety of world maps with various irregular wall shapes that were presented to it.

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 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 paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as function approximators.

Page 1. ORIGINAL ARTICLE Design of an advanced incremental fuzzy logic controller for laser surface heat treatments José Antonio Pérez & José Luis Ocaña & Carlos Molpeceres Received: 5 July 2006 /Accepted: 7 November ...

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.

In this paper, an energy system comprising three energy sources, namely PV, wind and fuel cells, is proposed. Each of the three energy sources is controlled so as to deliver energy at optimum efficiency. Fuzzy logic control is employed to achieve maximum power tracking ...

In this study, a new method is proposed for tuning the coefficients of PID-type fuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is called ''normalized acceleration'' and the other one is the classical ''error''. The ''normalized acceleration'' gives the ''relative rate'' information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID-type FLC using these two input variables. The integral coefficient is then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second-order system with varying parameters and time delay. r

Fuzzy logic control is one of the most interesting fields where fuzzy theory can be effectively applied. Fuzzy logic techniques attempt to imitate human thought processes in technical environments. In doing so, the fuzzy logic approach allows the designer to handle ...

The low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control. Soft computing technique-the Fuzzy logic is applied in this work .We have carried on with the hardware implementation for speed control of induction motor using the fuzzy logic control. Using the arduino micro controller, we have developed a hardware setup which is able to control the speed of induction motor by using Arduino Uno .We can conclude that we have been able to get a good control over the speed of motor.

Aim of this study is to propose a control strategy based on Simulated Annealing (SA) as a metaheuristic algorithm and Fuzzy Logic Controller (FLC) for semi-active nonlinear control of structures equipped with Magnetorheological (MR) dampers to prevent damage from severe dynamic loads such as earthquakes. MR dampers as promising semi-active control mechanisms are focus of attention for reducing seismic response of structures in the world of structural engineering. A fuzzy controller is applied to establish interactive relationships between structural responses and input voltages to the MR damper. A modified simulated annealing algorithm is employed as an evolutionary algorithm to design the fuzzy controller, which is herein described as a Modified Simulated Annealing-Fuzzy Logic Controller (MSA-FLC). Results of numerical simulations demonstrated the efficiency of the proposed smart strategy compared to similar methodologies recommended by other researchers.

In this paper we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) on the optimization of the membership functions' parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an autonomous wheeled mobile robot. The results obtained by the simulations performed are statistically compared among them and the previous work results obtained with GAs in order to find which is the best optimization technique for this particular robotics problem.

This paper presents the design of a sliding mode fuzzy logic-based controller for an active vehicle suspension system. The controller design is based on a Macpherson strut model and combines sliding mode techniques with fuzzy logic methodologies. This control ...

This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

In this study a fuzzy logic controller for mobile robot navigation has been designed. The designed controller deals with the uncertainty and ambiguity of the information the system receives. The technique has been used on an experimental mobile robot which uses a set of seven ultrasonic sensors to perceive the environment. The designed fuzzy controller maps the input space (information coming from ultrasonic sensors) to a safe collision-avoidance trajectory (output space) in real time. This is accomplished by an inference process based on rules (a list of IF-THEN statements) taken from a knowledge base. The technique generates satisfactory direction and velocity maneuvers of the autonomous vehicle which are used by the robot to reach its goal safely. Simulation and experimental results show the method can be used satisfactorily by wheeled mobile robots moving on unknown static terrains.

This manuscript deals with the simulation of DC-DC Zeta converter for high power drive application with greater efficiency, lesser losses and power factor correction. It involves simpler control circuitry with less external components. The explanation of Fundamental function of Zeta converter is given in this paper. To condense the harmonic content the PI, PID and Fuzzy Logic controller are used. The operation of Zeta converter in open loop, closed loop is obtained. Closed loop system of zeta converter proves better performance over open loop system. Open and closed loop circuits are simulated by using MATLAB simulink. By giving disturbance in closed loop and open loop systems, feat of Zeta converter is compared

This paper presents a dynamic model of a fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC) controller is used to control the active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation. K e y w o r d s: polymer-electrolyte fuel cell, dynamic model, residential power, fuzzy controller

In this paper the analytical comparison of brushless DC (BLDC) motor drive with proportional integral (PI) and fuzzy logic controller (FLC) based speed controllers is estimated. Proportional integral (PI) has disadvantages like it do not operate properly when the system has a high degree of load disturbances. In recent years, the application of fuzzy logic controller (FLC) for high dynamic performance of motor drives has become an important tool. FLC is a good for load disturbances and can be easily implemented. The modeling and simulation of both the speed controllers have been made by MATLAB/SIMULINK. The dynamic characteristics of the BLDC motor (speed and torque) response, obtained under PI and Fuzzy logic based speed controller, are compared for various operating condition [1]. 1. INTRODUCTION BLDC motor is defined as a type of self-synchronous rotary motor controlled by electronic commutation unlike DC motors have mechanical commutator. So BLDC motors do not have brushes but D...

Page 1. ORIGINAL ARTICLE Design of an advanced incremental fuzzy logic controller for laser surface heat treatments José Antonio Pérez & José Luis Ocaña & Carlos Molpeceres Received: 5 July 2006 /Accepted: 7 November ...

The fundamental equations governing the operation of a dc motor are straightforward and are well presented in most undergraduate curricula, usually in a required energy-conversion or electrical-machinery class at the junior level. The physical implications of the equations regarding device behavior, and the need for speed control, are easily understood. Furthermore, students can readily develop a mental picture of the physical motor and its response.

In this paper, several types of decomposed proportional-integral-derivative fuzzy logic controllers (PID FLCs) are tested and compared. An important feature of decomposed PID FLCs are their simple structures. In its simplest version, the decomposed PID FLC uses three one-input one-output inferences with three separate rule bases. Behaviours of proportional, integral and derivative PID FLC parts are defined with simple rules in proportional rule base, integral rule base and derivative rule base. The proposed decomposed PID FLC has been compared with several PID FLCs structures. All PID FLCs have been realised by the same hardware and software tools and have been applied as a real-time controller to a simple magnetic suspension system.

This paper describes the use of fuzzy logic techniques to control the speed of a three-phase induction motor. The use of Matlab/Simulink and fuzzyTECH MCU96 as software development tools for system design is emphasised. Hardware implementation is based on a standard 16/32-bit microcontroller, without the need of any additional components for the fuzzy logic controller. The system performance is evaluated in comparison with a traditional PI control scheme. Both simulation and experimental results are presented. ᭧

This paper introduces a neuro-fuzzy controller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performance of the proposed controller is verified by both simulations and

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

— Fuzzy Logic Controller (FLC) is best way in which this type of precision control can be accomplished by controller. During past twenty years significant amount of research using fuzzy logic has done in this field of control of non-linear dynamical system. Here we have developed temperature control system of ceramic infrared heater using fuzzy logic. Control theory techniques are the root from which convention controllers are deducted. The desired response of the output can be guaranteed by the feedback controller. A closed loop control system incorporating Fuzzy logic controller has been developed for a temperature regulation process. In this Fuzzy logic controller structure with an efficient realization and a small rule base that has been implemented in temperature control process. Fuzzy controller gives more accurate behavior in control action for temperature in heater as compare to PID Controller.

In this paper, at first a model of greenhouse is simulated with thermodynamic equations and then two greenhouse climate controllers (GHCC) are designed using a Fuzzy Logic controller and On/Off controller. The developed Fuzzy Logic Controller (FLC) prototype is based on Mamdani controller and it is built on the MATLAB software, then both of controllers are used with greenhouse model in simulation space of MATLAB software to understand the performance of each controllers and affection on the controlling parameters such as temperature and air humidity of inside the Greenhouse. The results show that the proposed Fuzzy Logic Controller is very user friendly, easy to design, highly adaptable and quick to perform.

Autonomous wheelchairs are important tools to enhance the mobility of people with disabilities. Advances in computer and wireless communication technologies have contributed to the provision of smart wheelchairs to suit the needs of the disabled person. This research paper presents the design and implementation of a voice controlled electric wheelchair. This design is based on voice recognition algorithms to classify the required commands to drive the wheelchair. An adaptive neuro-fuzzy controller has been used to generate the required real-time control signals for actuating motors of the wheelchair. This controller depends on real data received from obstacle avoidance sensors and a voice recognition classifier. The wheelchair is considered as a node in a wireless sensor network in order to track the position of the wheelchair and for supervisory control. The simulated and running experiments demonstrate that, by combining the concepts of soft-computing and mechatronics, the implemented wheelchair has become more sophisticated and gives people more mobility.

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.

The Webots commercial mobile robot simulation software and Khepera II miniature mobile robot have always been popular tools in research centers and universities. In this paper, the two items will be utilized as a platform for the investigation of Neural Q-Learning controllers. Webots remains as the primary simulation software where the simulated environment and robot are modeled. To cater for a wide variety of experiments, the simulation developed for the Khepera II is equipped with GUIs and various features. These functions allow the user to configure different environment and robot settings for different experiments. Then, the simulation is validated by comparing the behavior of the simulated and actual robot. As a result, a total of four controllers is proposed and tested on this platform. The designed controllers include both sensor and vision based controllers. These controllers are capable of exhibiting obstacle avoidance or wall following behaviors. In addition, an obstacle avoidance controller which is based on a combination of sensor and visual inputs via a fuzzy logic controller was proposed. Experimental results collected facilitate comparison and discussion of the algorithm and it further reveals that the mobile robot could successfully acquire the desired behavior.

A variety of approaches for path tracking control of wheeled mobile robots have been implemented. While most of these are based on controlling the robot dynamics, they are not applicable if the robot dynamics are inaccessible. In this paper, a fuzzy logic controller (FLC) for the path tracking of a wheeled mobile robot based on controlling the robot at a higher level is presented. The controller is highly robust and flexible and automatically follows a sequence of discrete waypoints, and no interpolation of the waypoints is needed to generate a continuous reference trajectory. The speeds are varied depending on the variations in the path and on the posture of the robot. The heuristic rules of the FLC are based on an analogy with a human driving a car and the optimization of the controller is based on experimentation. The implementation on a P3-AT mobile robot shows the effectiveness of the proposed approach.

Induction motor has been the workhouse in various industries, household purpose like washing machine, pumps etc. due to its low cost, low maintenance and well equipped construction features. This paper presents a smart speed control system for three phase induction motor using Neuro-fuzzy controller. Neuro-fuzzy controller plays a major role in improving the performance of the induction motor based on the settling time transient response dynamic response due to load variation and environmental conditions and set speed variation. The simulation results for the fuzzy logic controller and Neuro-fuzzy controller are compared. The results show the superiority of the Neuro-fuzzy controller in improving the performance of the three phase induction motor. The simulation design is tested using various toolboxes in MATLAB.

This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Induction motors are widely used in transportation, mining, petrochemical, manufacturing and in almost every other field dealing with electrical power. These motors are simple, efficient, highly robust and rugged thus offering a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to catastrophic failure of the machine in the long run. On-line condition monitoring of the induction motors has been widely used in the detection of faults. This paper delves into the various faults and study of conventional and innovative techniques for induction motor faults with an identification of future research areas.

This paper presents a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters. The mismatch between the characteristics which lead to varying performance is outlined. This paper also demonstrates certain similarities of both the fuzzy logic controller and sliding mode controller. Sensitivity of these controllers to supply voltage disturbances and load disturbances is studied and results are presented.

Fuzzy logic controllers (FLC) have gained popularity in the past few decades with successful implementation in many areas, including electrical machines' drive control. Many colleges are now offering fuzzy logic courses due to successful applications of FLCs in nonlinear systems. However, teaching students a fuzzy logic controlled drive system in a laboratory, or training technical staff, is time consuming and may be an expensive task. This paper presents an educational tool for fuzzy logic controlled brushless direct current motor (BDCM), which is a part of a virtual electrical machinery laboratory project. The tool has flexible structure and graphical interface. Motor and controller parameters of the drive system can be changed easily under different operating conditions. Index Terms-Brushless direct current motor (BDCM), educational tool, fuzzy logic controller (FLC).