Pid Controller Using Fuzzy Logic

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Dc motor speed control and performance analysis between pid  controller, pi controller and fuzzy logic controller

Dc motor speed control and performance analysis between pid controller, pi controller and fuzzy logic controller

The area of DC motor speed control and analysis in very wide, but Proportional-Integral-Derivative (PID) controllers have gained wide popularity in the control of DC motors. Their performances, though require some degree of manual tuning by the operator, are still satisfactory but a means of auto-tuning is desirable. In this paper, the performance of a select dc motor controlled by a proportional-integral-derivative (PID) controller and by a proportional integral (PI) controller is investigated. An overshoot is observed with an accompanied large settling time thereby confirming the behavior of a typical PID controller and PI controller. It is therefore a matter of necessity to tune the PID controller and PI controller in order to obtain the desired performance. On the other hand, a fuzzy logic based controller applied to the dc motor is investigated. With the application of appropriate expert rules, there is no overshoot and the settling time is within the desired value. With fuzzy logic controller, manual tuning is eliminated and intelligent tuning takes the Centre stage with satisfactory performance. Therefore in this paper we are comparing the performance of a select DC motor with the application of PID and PI controller and an auto fuzzy logic controller and observing the best result.
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Fuzzy Logic based PID Controller for pH Neutralization Process

Fuzzy Logic based PID Controller for pH Neutralization Process

There are many controller mechanisms used in a water treatment plant, narrowing down to an effective mechanism from among the many is a tough task for any process including the pH neutralization process. The selection of the control mechanism is fraught with difficulties because of the Control system design for pH neutralization is technically difficult to implement [27], owing to its non-linear responses, sensitive environment uncertain results and large number of requirements. Classical control mechanisms hold good only for linear, theory based processes. These simple mechanisms do not work as well when applied to constantly changing chemical systems with complex kinetic and thermodynamic reactions. These classical control mechanisms fail miserably when it comes to system performances and does not cover the whole range of operation.
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Voltage Control of Synchronous Generator Using PID  Fuzzy Logic Controller

Voltage Control of Synchronous Generator Using PID Fuzzy Logic Controller

is necessary. In case of digital PID controllers, the multiplication, integration and differentiation are performed numerically in digital computers. Users of control systems are frequently faced with the task of adjusting the controller parameters to obtain a desired behaviour. There are many different ways to do this. One way to do this is to go through the steps of modelling and control design. Since the PID controller has so few parameters a number of special empirical methods have also been developed. A simple idea is to connect a controller, increase the gain until the system starts to oscillate, and then reduce the gains by an appropriate factor. Another is to measure some features of the open loop response and to determine controller parameters based on these features. The PID controller is designed by using Ziegler and Nichols method.
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Speed Control of PMSM Motor Using Fuzzy and PID Controller

Speed Control of PMSM Motor Using Fuzzy and PID Controller

Fuzzy logic controller which is presented by zadeh in 1965 is a new controller [1]. Besides that, fuzzy logic controller is more efficient from the other controller such s proportional –integral (PI) controller. The comparison between them is needed to compare what the controller is efficient [2].the reason why conventional controller low efficiency such as PI controller because the overshoot is too high from the set point and it may takes delay time to get constant and sluggish response due

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Performance of synchronous generator using pid  fuzzy logic controller

Performance of synchronous generator using pid fuzzy logic controller

The PID controller has two zeros and one pole. Generally an additional pole is required to limit the high-frequency gain . To develop the control law for fuzzy logic controller, which performs the function of PID control efficiently, the concept of digital PID is necessary. In case of digital PID controllers, the multiplication, integration and differentiation are performed numerically in digital computers. Users of control systems are frequently faced with the task of adjusting the controller parameters to obtain a desired behaviour. There are many different ways to do this. One way to do this is to go through the steps of modelling and control design. Since the PID controller has so few parameters a number of special empirical methods have also been developed. A simple idea is to connect a controller, increase the gain until the system starts to oscillate, and then reduce the gains by an appropriate factor. Another is to measure some features of the open loop response and to determine controller parameters based on these features. The PID controller is designed by using Ziegler and Nichols method.
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Temperature Control of Semiconductor LASER Using Different Controllers

Temperature Control of Semiconductor LASER Using Different Controllers

In order to start the work, a literature survey has been carried out including some of the most important and common databases, namely the IEEE/IEE electronic library, IJSCE, IJARAI and IJICS. The survey spans over the last 27 years from (1988-2014). A number of publications discussing about semiconductor laser, PID, Fuzzy Logic and Fuzzy self- adaptive PID controller have been gone through. The proposed model and transfer function of semiconductor LASER has been in the research paper [1]. The wavelength of semiconductor LASER depends on the temperature i.e. if temperature of semiconductor LASER varies then wavelength of laser also vary. For the better performance of LASER, different types of controllers have been proposed in this thesis. The state of the art of PID controller, basic idea and particular issues discussed include specifications, stability, design, applications, and performance of PID control [6]. The traditional PID control obtain the desired results only in the system model parameters of non-time-variant, when applied to time-variant systems, the system performance will deteriorate, and even instability. Therefore, a Fuzzy logic controller introduces to control the temperature. The basic idea about fuzzy logic has been found in the research papers [4]. In these papers study, it has been shows that fuzzy logic controller gives the better performance than PID controller but the transient response is not better. Therefore, a new controller fuzzy self-adaptive PID controller has been introduced. The self adaptive PID controller adjusts its parameters automatically. The mathematical models and stability analysis of fuzzy self-adaptive PID have been presented in paper [2]. The comparative analysis of PID and fuzzy self-adaptive PID controller shows the better controller out of them [1]. Here a fuzzy control method has been implanted to control the temperature of semiconductor LASER system. However, due to some limiting nature of the fuzzy logic control a new improved technique has been applied known as the fuzzy self-adaptive PID control technique. Among these three type controllers fuzzy self-adaptive PID controller provides better result in controlling the temperature of semiconductor laser.
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IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER

IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER

This article presents the design and simulation of the ANFIS controller for better performance of the servomotor of a brushless DC motor (BLDC). Productivity BLDC servomotors based on ANFIS, fuzzy and PID controller are tested under different operating conditions, for example, changes in speed setting, parameter variations, load disturbance, etc. BLDC servo motors are used in the aerospace, control and measurement systems, electric vehicles, robotics and industrial control applications. In such cases, they are realized, as conventional P, PI and PID controllers of the control systems BLDC drive servo motors satisfactory transient and steady state responses. However, the main problem that arises with a conventional PID controller is that the parameters adjusted gain obtained from the drive control systems of the BLDC servo motor cannot produce a more transient response and a stable state under various operating conditions such as parameter variations, load disturbance, etc. In this Paper, design and implementation of the ANFIS controller and its performance compared to the PID controller and fuzzy controller to show its ability to monitor the errors and utility of ANFIS controller management applications. KEYWORDS: Brushless DC (BLDC) motor, PID controller, Fuzzy logic controller, ANFIS controller.
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Implementation of Fuzzy Pid Controller And Performance Comparision With Pid For Position Control Of Dc Motor

Implementation of Fuzzy Pid Controller And Performance Comparision With Pid For Position Control Of Dc Motor

Abstract:- This paper gives the demonstration about the position control of DC motor using a Fuzzy PID controller to meet the desired position in presence of set point changes the most commonly used controller in the industry field is the proportional-integral-derivative (PID) controller. The PID controllers mostly used in industries due to their robust performance in a wide range of operating conditions & their simple tuning methods. This paper presents design of PID controller with Ziegler-Nichols (ZN) technique for controlling the position of the DC motors. Fuzzy logic controller (FLC) provides an alternate to PID controller, especially when the available system models are inexact or unavailable. Fuzzy logic is one of the most successful applications of fuzzy set in which the variables are linguistic rather than numeric. The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The scopes includes the simulation results of the PID controller, implementation of fuzzy PID controller to position of DC motor In this paper fuzzy PID and proportional-integral-derivative (PID) controllers are compared for controlling the position of direct motors(DC) motors. Simulation results are demonstrated using MATLAB. Performance analysis results are carried out to analyze the effectiveness of the designed Fuzzy PID controller as compared to the ZN tuned PID controller.
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FLC BASED TUNINIG OF PID CONTROLLER FOR AN AUTOMATIC VOLTAGE    REGULATOR SYSTEM

FLC BASED TUNINIG OF PID CONTROLLER FOR AN AUTOMATIC VOLTAGE REGULATOR SYSTEM

of the controllers in the industrial process control applications are of PID type as no other controller match the simplicity, clear functionality, applicability and ease of use offered by the PID controller. The PID controller is used for a wide range of problems like motor drives, automotive, flight control, instrumentation etc. PID controllers provide robust and reliable performance for most systems if the PID parameters are tuned properly. Various tuning methods are explained in. Among the tuning methods, the Ziegler-Nichols (ZN) technique has been very influential. Ziegler-Nichols presented two tuning methods a step response method and a frequency response method. The frequency response method is more reliable than the step response method. The field of Fuzzy control has been making rapid progress in recent years. Fuzzy logic control has been widely exploited for nonlinear, high order & time delay system. This paper has two main contributions. Firstly, a PID controller has been designed for higher order system using Ziegler-Nichols frequency response method and its performance has been observed. The Ziegler Nichols tuned controller parameters are fine tuned to get satisfactory closed loop performance. Secondly, for the same system a fuzzy logic controller has been proposed with simple approach and smaller number of rules (four rules) as it gives the same performance as by the larger rule set Simulation results for a higher order system have been demonstrated. A performance comparison between Ziegler Nichols tuned PID controller, and the proposed fuzzy logic controller is presented. The paper has been explains generalized model of PID controller. Describes the design consideration for a higher order system. Design of PID controller using Z- N technique and design of fuzzy logic controller using simple approach and smaller rule base. Finally conclusion close the paper.
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Acidic Control of a Chemical Plant Implementing Non Linear Controllers

Acidic Control of a Chemical Plant Implementing Non Linear Controllers

This research is unique in incorporating the whole system control using LabVIEW software. This research presents PID, Fuzzy logic and sliding mode control for a pH neutralization pilot plant. Process modeling approach adopted in this research is based on the Physico-chemical principles and fundamental laws. PID controller gave a satisfactory response but the flow rate of the acid and base were extremely varying, this will decrease the lifetime of the control valves. The Fuzzy Logic Controller exhibited intelligence, which served in quick decision making efficiency of the plant. The sliding mode controller was the least affected by the ambient and environmental conditions which in turn increased the robustness of the system under study and hence the changes in real time were incorporated satisfactorily, but still exhibited chattering output in the flow rate response. This chattering feature of the output can be minimized by incorporating a Dynamic Sliding Mode Controller.
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PID - Fuzzy Logic Hybrid Controller for a Buck Converter

PID - Fuzzy Logic Hybrid Controller for a Buck Converter

stage to make the output to quickly settle to the desired value. Fuzzy logic controller provides fast rise time and less percentage of overshoot. PID controller provides low steady state error i.e. better accuracy [3]. Therefore a hybrid model is designed using the two controllers which improve the Performance of DC-DC Buck converter. Block diagram of hybrid model can depicted as figure 1.

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A 
		comparative study of fuzzy logic controllers for BLDC motor drive

A comparative study of fuzzy logic controllers for BLDC motor drive

Brushless D.C (BLDC) synchronous motors have been used in various fields of industrial applications for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of drive control [1]. Permanent Magnet Brushless DC (PMBLDC) motor is defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform [2]. BLDC motors do not have brushes for commutation. They are electronically commutated [3]. For the variable speed applications of BLDC motor, Proportional, Integral and Derivative (PID) motor control is commonly used control [4].Because; it has simple design and ease of control. However, its performance depends on proportional, integral and derivative gains [5- 6]. When the operating condition changes, the re-tuning process of control gains is necessary for dynamically minimize the total controller error. The various algorithms are used to find optimal PID controller parameters such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [7-10].Particle Swarm Optimization (PSO) and genetic algorithm (GA) is given based on population size, generation number, selection method, and crossover and mutation probabilities. There is no guarantee for finding optimal solutions for controllers within a finite amount of time. To overcome the problems in PID controller, fuzzy logic controller and hybrid fuzzy PID controllers can be designed for the speed control of BLDC motor. In this proposed research work, the speed control of BLDC motor was analyzed and its performance has been observed by using fuzzy logic controller and hybrid fuzzy PID [11- 13].The simulation results of two methods are studied and compared with conventional PI controller by using MATLAB/SIMULINK computational software. The simulation results of proposed controllers are used to show the abilities and shortcomings of conventional PI controller.
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Modeling and control of 6 DOF industrial robot using fuzzy logic controller

Modeling and control of 6 DOF industrial robot using fuzzy logic controller

PID controller is considered the most control technique that is widely used in control applications. A huge number of applications and control engineers had used the PID controller in daily life. PID control offers an easy method of controlling a process by varying its parameters. PID works well in industrial applications such as slow industrial manipulators were large components of joint inertia added by actuators. Since the invention of PID control in 1910, and Ziegler-Nichols’ tuning method in 1942 [7] and [18], PID controllers became dominant and popular issues in control theory due to simplicity of implementation, simplicity of design, and the ability to be used in a wide range of applications. Moreover, they are available at low cost. Finally, it provides robust and reliable performance for most systems if the parameters are tuned properly.
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A Comparative Application Of Performance Of The Sepic Converter Using Pi, Pid And Fuzzy Logic Controllers For Pmdc Motor Speed Analysis

A Comparative Application Of Performance Of The Sepic Converter Using Pi, Pid And Fuzzy Logic Controllers For Pmdc Motor Speed Analysis

Abstract—DC converters are widely used in industrial area. SEPIC converter is a suitable converter when the input voltage has got wide variations around the rated voltage. In this study presents an analysis of the dynamic performance on DC-DC SEPIC (Single Ended Primary Inductor Converter) converter which is driving Permanent Magnet DC Motor (PMDC Motor) with PI, PID and fuzzy logic controller implementation. One of the applications in which source side variations are wide is DC Motor applications. Therefore, importance is given to source side disturbance of SEPIC converter. Hence in this paper, a PID controller and fuzzy logic controller have been implemented to improve the dynamic and steady state performance of the SEPIC converter when source voltage is subjected to wide variations keeping the output voltage at a constant value at steady state. Simulation results have been obtained using MATLAB/SIMULINK. The percentage overshoot, rise time and settling time have been compared and analyzed.
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Tuning Of Conventional Pid And Fuzzy Logic Controller Using Different Defuzzification Techniques

Tuning Of Conventional Pid And Fuzzy Logic Controller Using Different Defuzzification Techniques

Abstract:- This paper presents a method for tuning of conventional PID controller. Simplicity, robustness, wide range of applicability and near-optimal performance are some of the reasons that have made PID control so popular in the academic and industry sectors. Recently, it has been noticed that PID controllers are often poorly tuned and some efforts have been made to systematically resolve this matter. Thus Fuzzy logi c can be used in context to vary the parameters values during the transient response, in order to improve the step response performances. Simulation analysis has been carried out for the different processes by conventional and different defuzzification techniques and the results indicate that the values of percentage overshoot are reduced by using fuzzy logic mechanism.
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Internal Model Control and Fuzzy Based PID Controller for Non-Linear Spherical Tank

Internal Model Control and Fuzzy Based PID Controller for Non-Linear Spherical Tank

ABSTRACT: In process industry the control of fluid level is required. The control of nonlinear process is difficult task. Numerous procedures are utilizing in spherical shaped tanks in contributes better waste for slurries and thick fluids. Thus, control of spherical shaped tank level is a testing undertaking because of its non-linearity and constantly changing cross-segment. This is because of connection between controlled variable level and controlled variable stream rate, which has a square root relationship. The primary goal is to execute the appropriate controller for spherical tank system to keep up the coveted level. System identification for the non-linear process is finished utilizing black box modeling and observed to be first order plus dead time (FOPDT) method. In this paper it is proposed to acquire the response of IMC based PID and fuzzy logic controller (FLC) based PID tuning for spherical tank system and to examine the system and the simulation results shows the Minimum rise time, Minimum peak time, minimum settling and Minimum integral square error (ISE).
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TEXTURE PATTERN IN ABNORMAL MAMMOGRAMS CLASSIFICATION USING SUPERVISED MACHINE 
LEARNING TECHNIQUES

TEXTURE PATTERN IN ABNORMAL MAMMOGRAMS CLASSIFICATION USING SUPERVISED MACHINE LEARNING TECHNIQUES

4257 (PID) control and fuzzy logic control (FLC). The method of PID control is simple and the effect of control is good which has been used widely in industrial application. Figure 1 shows the general block diagram of the system to be implemented in this paper. A conventional PID controller is a control loop feedback mechanism. The conventional PID controller has characteristics of simplicity, stable system and high reliability. The PID regulation law can gain a good control effect for quite a number of industrial control objects, especially for linear time-invariant systems control depends on each parameter setting of PID controller. However, PID control cannot set parameter via online. It cannot control well in non- linear and time-varying systems since the parameter is difficult to set [3].However, the methods of fuzzy logic controller (FLC) are getting more and more popular to improve the performance of the system. Therefore, it is necessary to compare the performance between PID control and fuzzy control.
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Design of PID, Fuzzy PD/PID and Single Input Fuzzy Logic Controller for Higher Order System

Design of PID, Fuzzy PD/PID and Single Input Fuzzy Logic Controller for Higher Order System

Conventional PID controllers have been a wide range of use in industry because of its simple structure and acceptable performance. This controller deals with both time response and frequency response improvements if they are properly tuned. But as the demands increases to control the different systems in industries, performance of conventional controllers are tend to degrade. Now systems are getting complicated day by day introducing higher order plants. There is drastic change in the performance of controllers with the introduction of Fuzzy systems and so the Fuzzy controllers (PD and PID) has been designed and tuned for third order system which is difficult to control by the use of conventional controllers. FLC has been widely used for nonlinear, high order & high dead time plants. This paper has three main considerations. Firstly, a PID controller has been designed for nonlinear unstable third order plant using Zeigler Nichols tuning method I & its performance is analyzed. Secondly, for the same system a FLC has been proposed with simple approach and smaller number of rules(four rules) as it gives the same performance by the larger set rule II . Even though modern control methods are very promising for non-linear control applications, they require substantial computational power because of complex decision making processes. For example FLC has to deal with fuzzification, rule base storage, inference mechanism and defuzzification operations. Larger set of rules yields more accurate control at the expense of longer computational time. Therefore it may not be practical because there are many implementation aspects that must be addressed, namely real-time response, communication bandwidth, computational capacity and onboard battery. The use of NN is also thought to be impractical due to its unpredictability, particularly when real time self-tuning is considered. Despite these issues, it is known that FLC requires simpler mathematics and offers higher degree of freedom in tuning its control parameters compared to other nonlinear controllers. In this paper, the Single Input Fuzzy Controller
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Design of a Robust Controller for Inverted Pendulum

Design of a Robust Controller for Inverted Pendulum

The model used in this paper is nonlinear in nature and are linearized using standard linearizing methods. Using this model, a set of transfer functions are derived which show the dynamics of the system. The same model is used to design various controller such as conventional PID controller, Fuzzy Logic Controller and H∞ Controller and comparative study of the performance is done in the face of model uncertainties, disturbances, rise time, settling time, maximum overshoot and finally we conclude that robust control using fuzzy Logic Controller is best controller compared to conventional and fuzzy controller.
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CONTROLLING PIDs USING NEURO FUZZY EXPERT SYSTEMS.

CONTROLLING PIDs USING NEURO FUZZY EXPERT SYSTEMS.

Proportional Integral Derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this paper, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been used for automatic tuning of PID controllers. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink.
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