Top PDF Speed Control of Separately Excited DC Motor using Fuzzy Logic Controller

Speed Control of Separately Excited DC Motor using Fuzzy Logic Controller

Speed Control of Separately Excited DC Motor using Fuzzy Logic Controller

Mamdani’s methods of the Fuzzy interface is the most commonly used method. It was among the first control systems built using fuzzy set theory. This inference method expects the output variable to be fuzzy sets. It is more advantageous to use a single membership function of a linguistic variable instead of number of fuzzy sets which can be tedious in some cases. This method of using a single linguistic variable in output is called as Singleton output mechanism. It enhances the Defuzzification process because it greatly simplifies the computation required by the more general Mamdani method which finds the centroid of the two dimensional function. But in the Sugeno type of inference can be used to model any inference system in which the output membership function is either linear or constant.
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Optimal PID Controller Design for Speed Control of a Separately Excited DC Motor: A Firefly Based Optimization Approach

Optimal PID Controller Design for Speed Control of a Separately Excited DC Motor: A Firefly Based Optimization Approach

This paper presents a new approach to determine the optimal proportional-integral-derivative controller parameters for the speed control of a separately excited DC motor using firefly optimization technique. Firefly algorithm is one of the recent evolutionary methods which are inspired by the Firefly’s behavior in nature. The firefly optimization technique is successfully implemented using MATLAB software. A comparison is drawn from the results obtained between the linear quadratic regulator and firefly optimization techniques. Simulation results are presented to illustrate the performance and validity of the design method.
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Speed Control of a Seperately Excited DC Motor by Implementing Fuzzy Logic Controller

Speed Control of a Seperately Excited DC Motor by Implementing Fuzzy Logic Controller

Separately excited dc motor as the name implies is an efficient type of DC motor. In this case both the armature winding on the rotor and field windings on the stator got energized with separate supplies, Armature is supplied with a variable voltage source and field winding with an independent source of constant voltage and there are no electrical connections between the two circuits, The effectiveness of this system is that the field current is independent of both load and armature current and thus helps in achieving the better-regulated speed, which is its main characteristics. Main advantages of dc motors are lying in its variable speed changes and control, which makes its way in modern control strategy. This will provide easy controllability and efficient performance. DC motors are almost used in various applications like steel rolling mills, electric vehicles and trains, robotic manipulators and industrial electronics. Speed control in DC motors was obtained by means of voltage control first in 1981 by Ward Leonard [1]. Currently famous and most frequent type of controller used in many industries is PID controller [2,3], but due to some hindrances, they face some complications and don’t offer better results when adaptive algorithms are in need.[4]. Due to the various disadvantages of conventional (PI and PID) controllers like delay in response and lack of proper action for error corrections, the Fuzzy Logic Controller makes its way and offers some fruitful solutions. Main advantages of FLC are that it doesn’t have a need of constructing a mathematical model of a system [5,6]. The effectiveness of the FLC is manifested by the fact that it puts an effective and simple implementation of human thinking into the actions of controlling algorithm [7]. Main important thing is that the FLC is only based on particular approximation and linguistic information [8]. Basic feedback controller is PID controller which possesses good performance. However is acceptable enough only with flexible and better tunings. In this work, FLC has been proposed for better improvement and analysis of the system functions.
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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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REAL TIME IMPLEMENTATION OF A FUZZY LOGIC CONTROLLER ON FPGA USING VHDL FOR DC MOTOR SPEED CONTROL

REAL TIME IMPLEMENTATION OF A FUZZY LOGIC CONTROLLER ON FPGA USING VHDL FOR DC MOTOR SPEED CONTROL

by toggle switches according to the requirement. Once this is done the ADC data will be read and Fuzzy Logic Controller implemented will calculate output value and the output of controller in term changes the duty cycle of PWM to increase or decrease the speed.IPM Module consists of Switching power converters are used in most DC motor drives to deliver the required energy to the motor. The energy that a switching power converter delivers to a DC motor is controlled by Pulse Width Modulated (PWM) signal applied to the gate of a power transistor coming from PWM module FPGA kit. Once the current speed equals the set speed, the motor starts running at the set speed. Again to change the set speed, the above procedure is repeated by changing the toggle switch position. As the set speed is varied, the ADC voltage also varies. It is observed that the current speed, which is displayed, on the ‘on board’ LCD display equals the set speed value. Fig. 10 shows the Photograph of the experimental setup and working model of FPGA based DC motor speed control system
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Comparison of Performance Measures of Speed Control for a DC Motor Using Fuzzy logic Controller and Optimal LQR Controller

Comparison of Performance Measures of Speed Control for a DC Motor Using Fuzzy logic Controller and Optimal LQR Controller

In control engineering, motor control plays a major role and is an unavoidable part, regardless of whether it is speed or position control. The effectiveness of a controller can be justified by performance objectives such as settling time, rise time, peak overshoot. Fuzzy control theory usually provides nonlinear controllers that are capable of performing different complex nonlinear control actions [1]. Due to the excellent speed control characteristics of a DC motor, it has been widely used in industry (such as cars, locomotives etc.) even though its maintenance costs are higher than the induction motor. As a result, authors have paid attention to position control of DC motor and prepared several methods to control speed of such motors. Proportional–Integral-Derivative (PID) controllers have been widely used for speed and position control [2]. A particle swarm optimization (PSO) instead of (GA).They presented a PID controller based on (PSO) method of tuning controller parameters. They modeled their PID-PSO controller in MATLAB environment and compare the results with fuzzy logic controller (FLC) using PSO. They found that PID-PSO controller gives better performance and minimal rise time than FLC-PSO controller [3]- [6], presented a novel optimal PID controller using (LQR) methodology in tuning the parameters of PID controller. The new PID controller is applied to control the speed of brushless DC motor (BLDC). Finally, the computer simulation and experimental results showed that the proposed controller gives better performance than the traditional controller [7], presented and compared two types of controllers which are PID controller and optimal controller. The PID compensator is designed using (GA), while the other compensator is made optimal and integral state feedback controller with Kalman filter. Computer simulations have been carried out. Finally they found that the second controller gives less settling, less overshoot and better performance encountering with noise and disturbance parameters variations. [8], presented a novel PID dual loop controller for a solar photovoltaic (PV) powered industrial permanent magnet DC (PMDC) motor drive. MATLAB/SIMULINK was used in the analysis for the GUI environment[9], introduced the optimal strategies for speed control of permanent magnet synchronous motor (PMSM) through the linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) methodologies. In this paper the fuzzy control schemes are used to perform a comparative study with LQR. The candidate controller is fuzzy PID controllers. The detailed derivation of these fuzzy controllers is referred to [10]-[11].
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PID Fuzzy Logic Controller System for DC Motor Speed Control

PID Fuzzy Logic Controller System for DC Motor Speed Control

PID Fuzzy Logic Controller System for DC Motor Speed Control. A good controller system must have resilience to disturbance and must be able to response quickly and accurately. Problem usually appears when PID controller system was built sensitively hence the system’s respon to the disturbance will yield big overshot/undershot then the possibility of oscillation to be happened is excelsior. When the controller system was built insensitively, the overshot/undershot will be small but the recovery time will be longer. Hybrid controller system could overcome those problems by combining PID control system with fuzzy logic. The main control of this system is PID controller while the fuzzy logic acts to reduce an overshot/undershot and a recovery time. The fuzzy logic controller is designed with two input error and delta error and one output of the motor speed. The output of fuzzy logic controller should be only half of the PID controller for limiting entirely fuzzy output. This hybrid system design has a better respon time controller system than PID controller without fuzzy logic.
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Speed control of separately excited DC motor using artificial intelligent approach

Speed control of separately excited DC motor using artificial intelligent approach

The dc motors are used in various applications such as defence, industries, Robotics etc. The preferences are because of their simplicity, ease of application, reliability and favourable cost have long been a backbone of industrial applications. DC drives are less complex with a single power conversion from AC to DC. DC drives are normally less expensive for most horsepower ratings. DC motors have a long tradition used as adjustable speed machines and a wide range of options have evolved for this purpose. In these applications, the motor should be precisely controlled to give the desired performance. Many varieties of control schemes such as P, proportional integral (PI), proportional derivation integral (PID), adaptive, fuzzy logic controller (FLCs) and Artificial Neural Networks (ANNs) have been developed for speed control of dc motors.
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Speed Control Enhancement Of A Chopper Fed Separately Excited Dc Motor Using Adaptive Neuro Fuzzy Controller

Speed Control Enhancement Of A Chopper Fed Separately Excited Dc Motor Using Adaptive Neuro Fuzzy Controller

D.C motor is considered as a SISO (Single Input and Single Output) system which has torque/speed characteristics and is compatible with most mechanical loads. By proper adjustment of the terminal voltage the mentioned characteristic makes a D.C motor controllable over a wide range of speeds [1]. In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based chopper fed speed control mechanism is proposed, for the enhancement of speed control of a chopper fed separately excited DC motor (SEDM). Computer Simulation isalso conducted to demonstrate the performance of the proposed controller and the conventional PI controller. The entire system is modeled using MATLAB Simulink 2012(b) software platform. A complete performance comparison between the proposed ANFIS based speed controller and conventional PI controller for different reference speeds is also presented.
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Performance Analysis of Separately Excited DC Motor using PI and FUZZY LOGIC Controller

Performance Analysis of Separately Excited DC Motor using PI and FUZZY LOGIC Controller

ABSTRACT: This paper contain the comparison analysis of two controllers (PI and Fuzzy logic controller) for speed control of dc motor using chopper circuit. The speed control is done by varying armature voltage using voltage controller , the firing angle of chopper is receive signal from controller and variable voltage is given to the armature of dc motor , the comparative result of both control scheme is graphically represented , the fuzzy logic controller reduces the current ripple and peak over shoot but is sluggish in nature , the simulation of result of both controller is presented in this paper using MATLAB/simulink
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SPEED CONTROL OF BRUSHLESS DC MOTOR ON RESONANT POLE INVERTER USING FUZZY LOGIC CONTROLLER

SPEED CONTROL OF BRUSHLESS DC MOTOR ON RESONANT POLE INVERTER USING FUZZY LOGIC CONTROLLER

Resonant pole inverters have been applied in Induction motor drive applications. They are usually required to change two phase switch states at the same time to obtain a resonant path. It is not suitable for a BDCM drive system as only one switch is needed to change the switching state in a PWM cycle. The switching frequency of three upper switches is different than that of three lower switches in an inverter for a BDCM drive system. All the Switches have the same switching frequency in a conventional inverter for induction motor applications. Therefore, it is necessary to develop a novel topology of soft-switching inverter and special control circuit for BDCM drive systems. This paper proposes a special designed resonant pole inverter that is suitable for BDCM drive systems and is easy to apply in industry. In addition, this inverter possesses the following advantages: low switching power losses, low inductor power losses, low switching noise, and simple control scheme
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Speed Control of Induction Motor using Fuzzy Logic Controller

Speed Control of Induction Motor using Fuzzy Logic Controller

The block model of the induction motor system with the controller be developed using the power system, power electronics, control system, signal processing toolboxes & from the fundamental functions available in the Simulink library in Matlab / Simulink. In this paper, plots of voltage, torque, speed, load & flux, etc are plotted as functions of time with the controller and the waveforms are observed on the equivalent scopes after running the simulations. The entire system modelled in Simulink is a closed loop feedback control system consisting of the plants, controllers, samplers, comparators, feedback systems, the mux, de-mux, summers, adders, gain blocks, multipliers, clocks, sub- systems, integrators, state-space models, subsystems, the output sinks (scopes), the input sources, etc. The developed simulink model for the control of various parameters of the SCIM is shown in the Fig 2.
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Simulation of speed control brushless dc motor using gaussian fuzzy logic controller

Simulation of speed control brushless dc motor using gaussian fuzzy logic controller

Direct Current (DC) motor was chosen for the speed control applications due to the control simplicity on the intrinsic decoupling between the flux and the torque. As the name implies, there are physical limitations to speed and life time because of brush wear. However, BLDC have been produced to overcome this problem. Since there are no carbon brushes to wear out, a BLDC motor can provide significantly greater life being now only limited by bearing wear. This advantage make BLDC motor becomes popular in the industry but this motor is a non-linear system hence, need more complex speed controller than the DC motor.
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Speed Control of Single Phase Induction Motor Using Fuzzy Logic Controller

Speed Control of Single Phase Induction Motor Using Fuzzy Logic Controller

The Proportional Integral (PI) controller of A.C drives are commonly employed in industries and many other applications, because of its simplicity, but it does not give high degree of speed control of single phase induction motor. The intelligent control systems become a powerful tool for control nonlinear system in present time. This paper proposes the Proportional Integral (PI) controller designed based on fuzzy logic system as an intelligent speed controller of single phase induction motor. In addition, The mathematical modeling and simulation single phase induction motor capacitor run type as asymmetrical two phase induction motor is represented too Also, two phase four leg voltage source PWM inverter with Sinusoidal Pulse Width Modulation (SPWM) switching technique is demonstrated and simulated. The overall system for the proportional-integral (PI) controller designed based on fuzzy logic system is simulated using MATLAB/Simulink environment. The results shows high response of the controller to the change of load which make a wide range of speed variation and make the speed return to its reference value.
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Speed Control of Separately Excited DC Motor Using Chopper

Speed Control of Separately Excited DC Motor Using Chopper

widely in position control system and variable speed drives. DC motors has excellent control of speed for speeding up and slowing down. In a DC motor the supply is directly connects to the field of the motor and as results a specific voltage control is obtained, which is necessary for control of speed and torque. DC drives are backbone of industrial applications due its advantages like simplicity Reliable, low cost, flexible etc. DC drives are cheaper for low horsepower ratings. DC motors are generally consider as flexible speed machines and a wide range of options have developed for this purpose.
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DC motor speed control using fuzzy logic controller

DC motor speed control using fuzzy logic controller

The speed of DC motors can be adjusted within wide boundaries so that this provides easy controllability and high performance. DC motors used in many applications such as still rolling mills, electric trains, electric vehicles, electric cranes and robotic manipulators require speed controller to perform their tasks. Speed controller of DC motors is carried out by means of voltage control in 1981 firstly by Ward Leonard [1]. The regulated voltage sources used for DC motor speed control have gained more importance after the introduction of thyristor as switching devices in power elctronics. Then semiconductor components such as MOSFET, IGBT, and GTO have been used as electric switching devices [2].
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Performance Comparison of Fuzzy Logic and PID Controller for Speed Control of DC Motor in Distribution Grid

Performance Comparison of Fuzzy Logic and PID Controller for Speed Control of DC Motor in Distribution Grid

controllers which is non-linear auto regressive [6]. In this paper, after giving dynamical model of the separately excited DC motor and providing basic information about fuzzy logic process, DC motor speed control is carried out by using PID controller and fuzzy logic controller. In order to maintain constant speed under various shaft load, the rules and sets of the fuzzy logic controllers are used and developed by using ‘IF-THEN’ rules, the speed errors are decreased in acceptable values. On the other hand, the gains of the PID controller tuned by using Simulink Tuning Tool. In the last part of the paper, the efficiency of both FLC and PID are compared in terms of settling time and overshoot percentage.
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Positioning Control of Separately Excited Dc Motor Drive by Using Self-Tuning Fuzzy-PID Controllers

Positioning Control of Separately Excited Dc Motor Drive by Using Self-Tuning Fuzzy-PID Controllers

ABSTRACT: This paper presents the implementation of a fuzzy logic controller and self-tuned proportional-integral- derivative (PID) controller on direct current (DC) motors positioning system. Simulation studies are carried out for self-tuned PID controller and FLC which are evaluated by using conventional rules & control knowledge base, for the armature-controlled with fixed field and field controlled with fixed armature current DC motors. The performance of the controllers is assessed on transient response and error integral criteria. The results presents that the FLC is superior in the rise time, speed fluctuations, and per cent overshoot as well as also much better in the controller output signal, which is much remarkable.
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Simulation of speed tracking for brushless DC motor using fuzzy logic controller

Simulation of speed tracking for brushless DC motor using fuzzy logic controller

Brushless DC motors are normally used in an automotive application, robotics, medical, industrial automation equipment and machine tools due to its advantages. The advantages are long life operation, easy to construct, noiseless operation and has a better speed performance. This project presents a simulation model for BLDC motor using MATLAB/Simulink. Usually, the speed control is achieved by using PI controller. Then, the dynamic response of the BLDC motor such as speed, torque as well as current and voltage are observed and been analyzed using the MATLAB model. It is quite hard and difficult to tune the conventional PI controller parameters. Therefore, a suitable speed controller is been developed by using MATLAB Fuzzy Logic tool box. The objectives of this project are to minimize the maximum overshoot (%OS), settling time (Ts) and also peak time (Tp). Besides, the purpose is to improve the speed performance of the motor drive system. In order to verify the effectiveness of the controller, both control algorithms (Fuzzy logic control and PID) are compared. The simulation results show that the FLC controller has better performance which has reduced and minimized the percentage of maximum overshoot, settling time, peak time and rise time compared with the used
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Development of DC DC converter for DC motor using fuzzy logic controller

Development of DC DC converter for DC motor using fuzzy logic controller

A standard approach for speed control in industrial drives is to use a proportional plus integral (PI) controller. Recent developments in artificial- intelligence-based control have brought into focus a possibility of replacing a Pi speed controller with a fuzzy logic (FL) equivalent [5]. Fuzzy Logic speed control is sometimes seen as the ultimate solution for high-performance drives of the next generation. Such a prediction of future trends is based on comparison of the drive response under PI and FL speed control, which has been compared on a number of occasions. Design of a speed controller is always based on the required response for a single operating point [5]. The existing comparisons fall into one of the two categories: speed response with PI and FL speed control for the design case is substantially different or the speed response is more or less the same [5].
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