scholarly journals Dissimilarity Clustering Algorithm for Designing the PID-like Fuzzy Controllers

2021 ◽  
Vol 45 (1) ◽  
pp. 267-286
Author(s):  
Essam Natsheh
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Fuzzy Logic Controller ◽  
Type Model ◽  
Performance Study ◽  
Process Industries ◽  
Research Fields ◽  
Dc Motors ◽  
Models Comparison ◽  
Improved Accuracy

Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

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.

Control of Rolling in Ships by Means of Active Fins Governed by a Fuzzy-Logic Controller

2001 ◽  
Vol 45 (04) ◽  
pp. 279-288
Author(s):  
Daniel A. Liut ◽  
Owen F. Hughes ◽  
Dean T. Mook
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Fuzzy Logic Controller ◽  
Vortex Sheet ◽  
Roll Motion ◽  
Moment Matching ◽  
Motion Simulation ◽  
Average Motion ◽  
Matching Procedure ◽  
Large Amplitude Motion

A fuzzy-logic controller is designed to govern the motion of fins in order to reduce roll motion in ships. The controller is trained by a moment-matching procedure. This procedure is also adapted to generate the fuzzy-logic rules by means of a clustering algorithm. The ship motion simulation is done by means of the program LAMP (Large Amplitude Motion Program), which can simulate the motion of a ship in a seaway. To model the fins we use a program called FINS, which is based on an unsteady vortex-sheet approach. Results are shown that prove the efficiency of the method, with average motion reductions up to 29% of the original values.

scholarly journals Fuzzy Logic Controller sebagai Penentu Gerak Mobile Robot Pembasmi Hama

2020 ◽  
Vol 1 (01) ◽  
pp. 19-24
Author(s):  
Muhammad Ridho Kenawas ◽  
Pola Risma ◽  
Tresna Dewi ◽  
Selamet Muslimin ◽  
Yurni Oktarina
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Robot Motion ◽  
Crop Failure ◽  
Driving System ◽  
Dc Motors ◽  
Autonomous Decision

A mobile robot is one of the solutions to overcome crop failure caused by chili pests. The mobile robot discussed in this paper is used to spray pesticide liquid into chili plant stems to prevent pests attack on the plants. This paper discusses the design of pesticide spraying robot motion with the application of Fuzzy Logic Controller. This robot employment is expected to reduce farmers' workload and to help to produce a good harvest.  Robot motions are divided into two conditions, which can be controlled by remote control as a controller (manual) and by means of a sensor (automatic). Mobile robot movements have a significant impact on navigation and the design of the driving system. Robot speed is controller by adjusting Pulse Width Modulation of DC motors attached to the robots' wheel, which set to be  90 for slow and 220  for high speed. The Fuzzy Logic Controller in this mobile robot functions as an autonomous decision-making driver to detect obstacles in front of the mobile robot and the targeted stems.

scholarly journals Real-time implementation of a novel hybrid fuzzy sliding mode control of a BLDC motor

2019 ◽  
Vol 10 (3) ◽  
pp. 1167
Author(s):  
Ali Mousmi ◽  
Ahmed Abbou ◽  
Yassine El Houm
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Fast Response ◽  
Bldc Motor ◽  
Tracking Accuracy ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors

<span lang="EN-US">This paper presents a novel hybrid control of a BLDC motor using a mixed sliding mode and fuzzy logic controller. The objective is to build a fast and robust controller which overcome classical controllers’ inconveniences and exploit the fast response of brushless dc motors characterized by an intense torque and fast response time. First the paper study pros and cons of both sliding mode and fuzzy logic controllers. Then the novel controller and its stability demonstration are presented. Finally the proposed controller method is used for the speed control of a BLDC motor 3KW. The obtained results are compared with those of a fuzzy logic and a conventional sliding mode controller. It allows to show performance of the proposed controller in terms of speed response and reaction against disturbances, which is improved more than 5 times without losing stability or altering tracking accuracy</span>

scholarly journals The Mobile Robot Control in Obstacle Avoidance Using Fuzzy Logic Controller

2020 ◽  
Vol 5 (3) ◽  
pp. 334-351
Author(s):  
M. Khairudin ◽  
R. Refalda ◽  
S. Yatmono ◽  
H. S. Pramono ◽  
A. K. Triatmaja ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Experimental Work ◽  
Fuzzy Logic Controller ◽  
Input Voltage ◽  
Robot System ◽  
Dc Motors ◽  
Robot Systems

A very challenging problem in mobile robot systems is mostly in obstacle avoidance strategies. This study aims to describe how the obstacle avoidance system on mobile robots works. This system is designed automatically using fuzzy logic control (FLC) developed using Matlab to help the mobile robots to avoid a head-on collision. The FLC designs were simulated on the mobile robot system. The simulation was conducted by comparing FLC performance to the proportional integral derivative (PID) controller. The simulation results indicate that FLC works better with lower settling time performance. To validate the results, FLC was used in a mobile robot system. It shows that FLC can control the velocity by braking or accelerating according to the sensor input installed in front of the mobile robot. The FLC control system functions as ultrasonic sensor input or a distance sensor. The input voltage was simulated with the potentiometer, whereas the output was shown by the velocity of DC motor. This study employed the simulation work in Simulink and Matlab, while the experimental work used laboratory scale of mobile robots. The results show that the velocity control of DC motors with FLC produces accurate data, so the robot could avoid the existing obstacles. The findings indicate that the simulation and the experimental work of FLC for mobile robot in obstacle avoidance are very close.

scholarly journals Development of a DSP Microcontroller-Based Fuzzy Logic Controller for Heliostat Orientation Control

2020 ◽  
Vol 10 (5) ◽  
pp. 1598
Author(s):  
Eugenio Salgado-Plasencia ◽  
Roberto V. Carrillo-Serrano ◽  
Manuel Toledano-Ayala
Keyword(s):  
Fuzzy Logic ◽  
Digital Signal Processor ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Low Cost ◽  
Angular Position ◽  
Digital Signal ◽  
Orientation Control ◽  
Dc Motors

This paper describes the design and implementation of a heliostat orientation control system based on a low-cost microcontroller. The proposed system uses a fuzzy logic controller (FLC) with the Center of Sums defuzzification method embedded on a dsPIC33EP256MU806 Digital Signal Processor (DSP), in order to modify the orientation of a heliostat by controlling the angular position of two DC motors connected to the axes of the heliostat. The FLC is compared to a traditional Proportional-Integral-Derivative (PID) controller to evaluate the performance of the system. Both the FLC and PID controller were designed for the position control of the heliostat DC motors at no load, and then they were implemented in the orientation control of the heliostat using the same controller parameters. The experimental results show that the FLC has a better performance and flexibility than a traditional PID controller in the orientation control of a heliostat.

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