scholarly journals Implementation of reduced induction machine fuzzy logic control based on dSPACE-1104 R&D controller board

2021 ◽  
Vol 12 (2) ◽  
pp. 1015
Author(s):  
Mohamed Moutchou ◽  
Atman Jbari ◽  
Younes Abouelmahjoub
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Induction Machine ◽  
Good Choice ◽  
Control Field ◽  
Logic Control ◽  
Dspace 1104 ◽  
Fuzzy Logic Theory ◽  
Controller Board

In this paper, we present our contribution in Induction Machine control field. The control we designed is based on fuzzy logic theory, this choice is motivated by the fact that this technique of control is suitable for the control of systems characterized by its parameters uncertainties and variations. The proposed control is optimized by using a genetic algorithm for fuzzy logic controller (FLC) gains tuning and by a good choice of calculation techniques used in FLC. Three versions of IM fuzzy logic control were validated by simulation. After that in order to be able to experimentally implement this control on dSPACE-1104, we proposed an optimized and reduced structure of the control. The experimental results proof the effectiveness and the satisfied performance of the proposed IM fuzzy control.

Fuzzy Logic Control

1993 ◽  
pp. 20-40 ◽  
Author(s):  
Marzuki Khalid ◽  
Sigeru Omatu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Bath Temperature ◽  
Water Bath ◽  
Temperature Control System ◽  
Clear Understanding ◽  
Logic Control ◽  
Fuzzy Logic Theory

Although fuzzy logic theory was proposed about three decades ago, it is only until recently that fuzzy control technology has been successfully applied to many industrial systems and domestic appliances. Much of these developments is mainly due to a better understanding of the concept of fuzzy logic, its simplicity of implementation, and its feasibility in hardware development. This article intends to provide a simple but clear understanding on the concept of fuzzy logic and its application to control systems (therefore not intended for those who have already understood the basic principles of fuzzy logic control). A simulation example is given on the development and operation of a fuzzy logic controller for a multi-variable water bath temperature control system. C-pseudocodes are given in the Appendices to clarify the water bath fuzzy control algorithms.

Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM)

2015 ◽  
Vol 4 (4) ◽  
pp. 139 ◽  
Author(s):  
Loukal Keltoum ◽  
Benalia Leila
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Induction Machine ◽  
Operating Conditions ◽  
Logic Control ◽  
Doubly Fed Induction Machine ◽  
Doubly Fed ◽  
Interval Type

The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases have established their robust and that their performance is less sensitive to parameter variations over conventional controllers. In this paper, Interval Type-2 Fuzzy Logic Controller (IT2FLC) method is proposed for controlling the speed with a direct stator flux orientation control of doubly-fed induction motor (DFIM), we made a comparison between the Type-1 Fuzzy Logic Control (T1FLC) and IT2FLC of the DFIM, first a modeling of DFIM is expressed in a (d-q) synchronous rotating frame. After the development and the synthesis of a stabilizing control laws design based on IT2FLC. We use this last approach to the control of the DFIM under different operating conditions such as load torque and in the presence of parameter variation. The obtained simulation results show the feasibility and the effectiveness of the suggested method.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Comparison of a Fuzzy Logic Controller With a P + D Control Law

1989 ◽  
Vol 111 (2) ◽  
pp. 128-137 ◽  
Author(s):  
S. Daley ◽  
K. F. Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Good Control ◽  
Control Law ◽  
Dynamic Coupling ◽  
Process Knowledge ◽  
Logic Control ◽  
Limited Process

A study is described that compares the performance of a self-organizing fuzzy logic control law (SOC) with that of the more traditional P + D algorithm. The multivariate problem used for the investigation is the attitude control of a flexible satellite that has significant dynamic coupling of the axes. It is demonstrated that the SOC can provide good control, requires limited process knowledge and compares favorably with the P + D algorithm.

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.

scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

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.

Effective Design of Active Suspension System using Fuzzy Logic Control Approach

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Mohd Avesh ◽  
Rajeev Srivastava ◽  
Rakesh Chandmal Sharma ◽  
Neeraj Sharma
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Active Suspension ◽  
Suspension System ◽  
Ride Quality ◽  
Hardware Complexity ◽  
Control Approach ◽  
Logic Control ◽  
Rules Set

The study deals with the light passenger vehicle suspension system design to improve the ride quality. The fuzzy logic control approach is applied to the half car suspension system model by adjusting the control parameters and properties using online adaptation with a minimized cost function and reduced hardware complexity. The performance of resulting model is tested under the influence of trapezoidal and triangular membership functions using the 9, 25 and 49 rules-set. The controller robustness is observed at different performance indices. Road excitations in the form of disturbance input are modelled as the sinusoidal function of a speed bump to reveal the transient response of the automotive body. Ultimately, the performance of active suspension system has been improved in terms of displacement and acceleration of seat, heave, pitch, and roll by the application of proposed fuzzy logic controller. Results reported that the trapezoidal shape 25 rules set membership function based fuzzy logic controller gives the best performance between the investigated systems.

Interval Type-2 Fuzzy Logic Control of NM70 Shape Memory Actuator

2014 ◽  
Author(s):  
Ireneusz Dominik
Keyword(s):  
Fuzzy Logic ◽  
Shape Memory ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Interval Type ◽  
Nonlinear Plant

The main aim of this article is to present the usage of type-2 fuzzy logic controller to control a shape memory actuator. To enhance real-time performance simplified interval fuzzy sets were used. The algorithm was implemented in the ATmega32 microcontroller. The dedicated PC application was also built. The fuzzy logic controller type-2 was tested experimentally by controlling position of the shape memory alloy actuator NM70 which despite its small size distinguishes itself by its strength. The obtained results confirmed that type-2 fuzzy controller performed efficiently with a difficult to control nonlinear plant. The research also proved that interval type-2 controllers, which are a simplified version of the general type-2 controllers, are very efficient. They can handle uncertainties without increasing drastically the computational complexity. Experimental data comparison of the fuzzy logic controller type-2 with type-1 clearly indicates the superiority of the former, especially in reducing overshooting.

scholarly journals Power System Stabilization By Fuzzy Set Theory Based Control Of SMES

1970 ◽  
Vol 6 (2) ◽  
pp. 33-41 ◽  
Author(s):  
Md Rafiqul Islam Sheikh ◽  
Rion Takahashi ◽  
Junji Tamura
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Power System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Magnetic Energy ◽  
Dynamic Performance ◽  
Superior Performance ◽  
Logic Control

At present fuzzy logic control is receiving increasing emphasis in process control applications. The paper describes the application of fuzzy logic control in a power system that uses a 12- pulse bridge converter associated with Superconductive Magnetic Energy Storage (SMES) unit. The fuzzy control is used in both the frequency and voltage control loops, replacing the conventional control method. The control algorithms have been developed in detail and simulation results are presented. These results clearly indicate the superior performance of fuzzy control during the dynamic period of energy transfer between the power system and SMES unit. Keywords: Fuzzy logic controller; power system dynamic performance; SMES unit. DOI: http://dx.doi.org/10.3329/diujst.v6i2.9343 DIUJST 2011; 6(2): 33-41

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