scholarly journals Optimization of DC motor speed control based on fuzzy logic-PID controller

2021 ◽  
pp. 143-153
Author(s):  
Amer Farhan Sheet ◽  
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Fuzzy Rules ◽  
Motor Speed ◽  
Speed Error ◽  
Self Tuning ◽  
The Difference

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

scholarly journals PERFORMANCE IMPROVEMENTS OF SEPARATELY EXCITED DC MOTOR USING FUZZY LOGIC CONTROL

2020 ◽  
Vol 6 (2) ◽  
pp. 47-58
Author(s):  
R. Nagarajan ◽  
M. Gokulkannan ◽  
T. Dinesh ◽  
S. Murugesan ◽  
M. Naveenprasanth
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Speed Control ◽  
Dc Motor ◽  
Fuzzy Rules ◽  
Motor Speed ◽  
Performance Improvements ◽  
Speed Performance

This paper demonstrates the importance of a fuzzy logic controller over conventional method. The performance of the separately excited DC motor is analyzed by using fuzzy logic controller (FLC) in MATLAB/SIMULINK environment. The FLC speed controller is designed based on the expert knowledge of the fuzzy rules system. The proposed DC motor speed control fuzzy rules are designed for fuzzy logic controller. The output response of the system is obtained by using fuzzy logic controller. The designed fuzzy controller for speed control performance is investigated. Significantly reducing the overshoot and shortening the settling time of the speed response of the motor. They validate different control of approaches, the simulation results show improvement in motor efficiency and speed performance.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller

2015 ◽  
Author(s):  
Wafa Batayneh ◽  
Nash’at Nawafleh
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
The Neural Network ◽  
Intelligent Controllers ◽  
Rising Time

This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to the conventional PID controller. The performance of the designed Neural Networks, Fuzzy logic controller and the PID controller is compared and investigated. Finally, the results show that the neural network has minimum overshoot, and minimum steady state parameters. This shows more efficiency of the intelligent controllers over the conventional PID controller. Also it shows that Neural Networks is better than Fuzzy logic controller in terms of over shoot and rising time. At the end of this paper an implementation of Graphical User Interface (GUI) method is developed. The main purpose of the GUI is to give the users a chance to use the program in a simple way without the need to understand the program languages.

Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments

2014 ◽  
Vol 3 (2) ◽  
pp. 84
Author(s):  
P. J. Ragu
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Lab View ◽  
Adaptive Fuzzy Logic ◽  
Self Tuning ◽  
Input Variables ◽  
Temperature Errors

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning  Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

scholarly journals Online Position Control Performance Improving Applying Incremental Fuzzy Logic Controller

2018 ◽  
Vol 248 ◽  
pp. 02005
Author(s):  
Dirman Hanafi ◽  
Mohamed Najib Ribuan ◽  
Wan HamidahWan Abas ◽  
Hidayat ◽  
Elmy Johana ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Motor Performance ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Peak Time ◽  
Control Performance ◽  
Position Control System

This paper presents the online control system application for improving the DC motor performance. DC motor widely used in industries and many appliances. For this aim fuzzy logic controller is applied. The type of fuzzy controller use is an incremental fuzzy logic controller (IFLC). The IFLC is developed by using MATLAB Simulink Software and implemented in online position control system applying RAPCON board as a platform. The experimental results produced the best gains of the IFLC are 1.785, 0.0056955 and 0.01 for error gain (GE), gain of change error (GCE) and gain of output (GCU) respectively. Its produce smaller rise time, peak time, 0% overshoot and smaller settling time. Beside that the IFLC response also able to follow the set point. The controller response parameters values are also acceptable. It means that the IFLC suitable to be use for improving the position control system performance.

scholarly journals Optimal Tuning of Fuzzy Logic Controller Based Speed Control of DC Motor

2021 ◽  
Vol 19 (1) ◽  
pp. 77-80
Author(s):  
Mohsin A. Koondhar ◽  
Muhammad U. Keerio ◽  
Rameez A. Talani ◽  
Kamran A. Samo ◽  
Muhammad S. Bajwa ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Dc Motors ◽  
Manual Adjustment ◽  
Control Application ◽  
Installation Time ◽  
Transient And Steady State

Fuzzy logic controller (FLC) has become popular in the speed control application of DC motors with automatic adjustment function. In this article, the performance of a specific FLC controlled DC motor is studied. The exceed speed is observed with a stabilization time, thus confirming the FLC behavior. Therefore, FLC must be set to obtain the required performance by applying appropriate expert rules, the minimum overshoot and installation time can be maintained within the required values. With the help of FLC, the manual adjustment function is gradually eliminated, and the intelligent adjustment function is at the center position, and the performance is satisfactory. FLC DC motor speed control is implemented in MATLAB environment. The results show that the FLC method has the smallest bypass, smallest transient and steady-state error, and shows higher FLC efficiency as compared with other conventional controllers.

scholarly journals Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

2018 ◽  
Vol 9 (4) ◽  
pp. 1967 ◽  
Author(s):  
Nabil Farah ◽  
M. H. N. Talib ◽  
Z. Ibrahim ◽  
J. M. Lazi ◽  
Maaspaliza Azri
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Experimental Testing ◽  
Motor Drives ◽  
Computational Burden ◽  
Response Characteristics ◽  
Speed Error ◽  
Self Tuning ◽  
Takagi Sugeno

<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

scholarly journals SPEED CONTROL OF DC MOTOR ON LOAD USING FUZZY LOGIC CONTROLLER (A CASE STUDY OF EMERGENCY LUBE OIL PUMP MOTOR OF H25 HITACHI TURBINE GENERATOR)

2017 ◽  
Vol 36 (3) ◽  
pp. 867-875
Author(s):  
II Ekpoudom ◽  
IE Archibong ◽  
UT Itaketo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Project Work ◽  
Maximum Speed ◽  
Operating Pressure ◽  
Motor Speed ◽  
Turbine Generator ◽  
System A

This paper presents the development of a fuzzy logic controller for the driver DC motor in the lube oil system of the H25 Hitachi gas turbine generator. The turbine generator is required to run at an operating pressure of 1.5bar with the low and the high pressure trip points being 0.78 bar and 1.9 bar respectively. However, the driver DC motor speed drifted from the desired speed of 1450 revolutions per minutes (rpm) to as low as 1414 rpm. It is against this backdrop, that this project work was envisaged to design a controller capable of controlling the speed of the DC motor in order to achieve the desired speed rating of 1450 rpm. In modelling the motor, the transfer function method was used to develop a linear approximation to the actual motor. After computing the total inertia of the motor shaft, the motor model was simulated for the speed response in MATLAB and Simulink environment, and the response showed that the motor attained an actual maximum speed of 1414 rpm at settling time of 0.3 seconds.  Based on expert knowledge of the lube oil system, a fuzzy logic controller was designed and this resulted in the issuance of a control action to correct the actual speed of the motor from 1414 rpm to the desired speed of 1450 rpm.  http://dx.doi.org/10.4314/njt.v36i3.29

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