scholarly journals Designing driving and control circuits of four-phase variable reluctance stepper motor using fuzzy logic control

2017 ◽  
Vol 100 (2) ◽  
pp. 695-709 ◽  
Author(s):  
Auday Al-Mayyahi ◽  
Ramzy S. Ali ◽  
Rabee’ H. Thejel
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Stepper Motor ◽  
Phase Variable ◽  
Variable Reluctance ◽  
Logic Control ◽  
Control Circuits ◽  
And Control

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

Use of Linguistic Fuzzy-Logic Control for Mechatronic System Modelling and Control

2015 ◽  
Vol 816 ◽  
pp. 3-8 ◽  
Author(s):  
Radim Farana ◽  
Bogdan Walek ◽  
Michal Janošek ◽  
Jaroslav Žáček
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Intelligent Systems ◽  
System Modelling ◽  
Mechatronic System ◽  
System Description ◽  
Logic Control ◽  
Input And Output ◽  
The University ◽  
And Control

The article presents use of a linguistic fuzzy-logic control (LFLC) system for mechatronic system modelling and control. The presented applications were verified on real laboratory tasks in the Laboratory of Intelligent Systems at the University of Ostrava. The LFLC system was developed at the University of Ostrava, Institute for Research and Applications of Fuzzy Modeling. This technology enables users to describe the system behaviour and/or the control strategy as a set of fuzzy rules. Input and output variables scales are defined by contexts and their change allows using the same system description for systems with similar behaviour very easily.

A Simple Fuzzy Logic Diagnosis System for Control of Internal Combustion Engines

2015 ◽  
Vol 74 (5) ◽  
Author(s):  
Mohammad Javad Nekooei ◽  
Jaswar Jaswar ◽  
A. Priyanto
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
High Speed ◽  
Internal Combustion Engines ◽  
Fault Tolerant ◽  
Combustion Engines ◽  
Normal Range ◽  
Logic Control ◽  
Linear Problems ◽  
And Control

Fuzzy logic (FL) systems are widely established as a technology offering an alternative system to tackle compound and ill defined problems. They can be trained from examples, are fault tolerant in the sense that they are capable to grip noisy and deficient data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. in this paper a simple fuzzy logic control has been developed which is used for defining engine system faults and control and maintain them in a normal range  without use any complicated mathematical equation and any fault sensor.  

Study of the Parallel Hybrid Electric Vehicle Drive Train Fuzzy Logic Control Strategy

2013 ◽  
Vol 645 ◽  
pp. 413-417 ◽  
Author(s):  
Yun Zhang ◽  
Xiu Min Yu ◽  
Ming Xuan Bi
Keyword(s):  
Fuzzy Logic ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Drive Train ◽  
Logic Control ◽  
Parallel Hybrid ◽  
And Control

Vehicle power train plays an important role to develop the energy efficient and reduce fuel consumption.In this paper,the drive train fuzzy controller structure and the fuzzy logic control strategies of parallel hybrid vehicles is presented. Simulation results illustrate the potential of the proposed controller and control strategy in terms of fuel economy and in keeping the deviations of SOC at a low level.

An Algorithm for Designing Conventional and Fuzzy Logic Control Systems

1997 ◽  
Author(s):  
Dean B. Edwards ◽  
John R. Canning
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Pid Control ◽  
Fuzzy Logic Control ◽  
Design Parameters ◽  
Input State ◽  
State Variables ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
And Control

Abstract This paper presents an algorithm that can be used to design either conventional or fuzzy logic control systems. In order to use the algorithm, the engineer must first choose a performance index for the system which he or she wants to optimize relative to some specified design parameters. For conventional state space controllers, the design parameters are the feedback constants associated with the state variables of the system. For fuzzy logic controllers, the design parameters are the parameters used to define the fuzzy sets for the input state and control variables. We use the algorithm to design proportional plus derivative (PD) and proportional, integral, and derivative (PID) control systems and their equivalent fuzzy logic control systems. The algorithm therefore provides a unifying approach for designing either conventional or fuzzy logic control systems.

scholarly journals Digital Control Fuzzy Logic for a Water Tank Level Using Arduino

2021 ◽  
Vol 1 (1) ◽  
pp. 2-10
Author(s):  
Fayçal CHABNI ◽  
Rachid TALEB ◽  
Abderrahmen BENBOUALI ◽  
Mohammed Amin BOUTHIBA
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
High Performance ◽  
Low Cost ◽  
Industrial Applications ◽  
Water Tank ◽  
Level Control ◽  
Practical Procedure ◽  
Logic Control ◽  
And Control

Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of fuzzy logic controls are still implemented on expensive high performance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low cost controller applied to water level control system. The paper also gives a low cost hardware solution and practical procedure for system identification and control. We started, first by identifying the process to obtain its mathematical model. Then we used two methods to control our system (PI and fuzzy control). Simulation and experimental results are presented.

Sign in / Sign up

Export Citation Format

Share Document