4th International Conference on Automatic Control and Mechatronic Engineering (ICACME 2015)

  • 29-30 May 2015
  • Kuala Lumpur, Malaysia

Description

Topics
  • Control Theory and Application
    • Optimization Problems in Control Engineering
    • Machines and Mechanical Engineering
    • Control System Modeling
    • Cybernetics
    • Automation in Chemical Engineering
    • Process Control and automatic control theory
    • Automation technology application
    • Automotive Control Systems and Autonomous Vehicles
    • Man-Machine Interaction
    • System Engineering Theory and Practice
    • Circuits and Electronics for Control
    • Manufacturing Systems Control
    • Simulation Techniques and Methodologies
    • Control Theory and Methodologies
    • Signal Processing Systems for Control
    • Intelligent Optimization Algorithm and Application
  • Automatic Control and Technology
    • Guidance, Navigation and Control
    • Hybrid Systems and Control
    • Electronics Automation and Electrical Engineering
    • Power machinery engineering
    • Electronic, communication and automatic control technology
    • Predictive Control, Intelligent Control and Servo Control
    • Automation instrument and device
    • Simulation technology of control system
    • Signal Processing Systems for Control
    • Networked Control Systems
    • Linear, Nonlinear and Fuzzy Systems and Control
    • Stochastic Systems Control and Remote Supervisory Control
    • PLC and Micro-controllers
  • Mechatronic Engineering
    • Laser processing technology and system
    • Image recognition and intelligent control
    • Manufacturing Process Simulation
    • Design and Operations of Manufacturing Systems for Responsiveness
    • CIMS and Manufacturing System
    • Mechanical and Liquid Flow Dynamic
    • Complex mechanical-electro-liquid System
    • Hybrid Macro/Meso/Micro Manufacturing Processes
    • Advanced manufacturing technology
    • Hydraulic servo system
    • Theory, technology and equipment of precision forming and special machining
    • Theory and technology of optical-mechanical-electrical integration
    • Collaborative and Digital Manufacturing for Advancing Product Quality
    • Development and Applications of Micro Manufacturing Equipment
    • CAD, CAM & CAE
    • Miniaturization of Molding and Casting Processes
    • Applications of Micro and Nano Systems
    • Diagnostics, Performance Prediction and Decision Making for Intelligent Maintenance of Manufacturing Systems

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Science: Engineering, Physics
Technology: Materials, Nanotechnology, Robotics

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Mustafa RasemAbuzeid1

 Nasr E Shtawa2

1Aljabal Algharbi University, Faculty of Engineering - Gharian, Dept. Of Electrical Eng.

2University of Tripoli, Faculty of Engineering,  Dept. of Electrical Eng.


Abstract 

This paper presents the most commonly used controller in the industry field is the (PID) controller. Fuzzy logic controller (FLC) provides an alternative to PID controller. The purpose of this paper is to design a Fuzzy Logic controller to improve the performance (speed) of the DC motor in order to control the angular speed of the motor. The purpose of Fuzzy Logic controller is to improve the performance of DC motor by controlling the speed of the motor using different system and to show that the Fuzzy Logic controller can be used on the DC motor control.

1-Introduction  

The speed of DC motor can be adjusted to a great extent so as to provide easy control and high performance. There are several conventional and numeric controller types intended for controlling the DC motor speed at its executing various tasks, PID Controller, Fuzzy Logic Controller [1]. The terms fuzzy logic is used with a number of deferent meanings. Fuzzy logic has been successfully applied to many control problems, more than any other areas of applications. Various fuzzy programming tools have been developed to facilitate fuzzy rule of control. Fuzzy logic developed by Zadech is applied for controller design in many applications. A fuzzy logic controller (FLC) was proved analytically to be equivalent to a nonlinear PI controller when a nonlinear defuzzification method is used. The result from the comparisons of conventional and

fuzzy logic controller techniques in the form of FLC and fuzzy compensator also showed fuzzy logic can reduce the effects of nonlinearity in a DC motor and improve the performance of a controller [2].