scholarly journals Implementation of a Fuzzy Logic Controller for an Aerothermic Process

2020 ◽  
Vol 9 (3) ◽  
pp. 3584-3588
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Control Methods ◽  
Industrial Systems ◽  
Control Techniques ◽  
Logic Control ◽  
Linear Behaviour ◽  
Time Required

The objective of any controller design is to maintain the set point value despite its variation, with a good rejection of various disturbances that can infect the system to be controlled and to minimize the energy consumption. To achieve these objectives, several control methods are proposed in the literature. Nevertheless, due to the non-linear behaviour of most industrial systems, fuzzy logic control remains the most appropriate method to control this type of system. This article compares two fuzzy logic control techniques having two inputs and one output. These methods are applied to an aerothermic process in our laboratory. The obtained experimental results allowed us to achieve the main control objectives, such asset-point trackingand regulation. These results encouraged us to exploit the advantage of each technique, in order to make the controller design simpler and to minimize the time required to calculate the command applied to the process.

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 DESIGN AND DEVELOPMENT OF FAST CHARGING BATTERY USING FUZZY LOGIC CONTROL TECHNIQUE

2019 ◽  
Vol 1 (1) ◽  
pp. 19
Author(s):  
Muhammad Nizam ◽  
Naji Abdalaziz Ali
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Cycle Performance ◽  
Control Technique ◽  
Battery Life ◽  
Battery Charger ◽  
Control Techniques ◽  
Logic Control ◽  
Battery Chargers ◽  
Fast Charging

<span lang="EN-US">Battery charging is an important issue when it is associated with battery life and cycle performance. The aim of this research is to design and promote fast battery chargers using fuzzy logic control techniques (FLC) for LiFePO4 batteries have been developed. The proposed charger is controlled by voltage and current to activate the PWM duty. The results show that the proposed battery charger has the potential to accelerate charging up to 37% at the rate when charging 2C. This means it is faster than the existing filling. The charger proposed by the FLC method is also capable of charging LiFePO4 batteries with greater efficiency, which is 82%. It can be concluded that the FLC application method has better performance than the CC-CV method</span>

Fuzzy Logic Control of Time Periodic Mechanical Systems

1997 ◽  
Author(s):  
Dan Boghiu ◽  
S. C. Sinha ◽  
Dan B. Marghitu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Inverted Pendulum ◽  
Controller Design ◽  
Mechanical Systems ◽  
Elastic Beam ◽  
Vertical Position ◽  
Logic Control ◽  
Short Period ◽  
Time Periodic

Abstract The fuzzy logic control of mechanical systems with periodic coefficients is considered. The controller design is illustrated through two examples, which include linear as well as nonlinear systems. For the linear case, a controller is designed such that a single inverted pendulum with a time periodic follower force is stabilized in the vertical position. As an example of the nonlinear system, the flap motion of a parametrically excited rotating elastic beam is considered. The controller is designed such that the deflection of the beam tip vanishes in a short period of time.

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.

Computer-Aided Learning of Controller Design: Focus on Fuzzy Logic Control

2002 ◽  
Vol 39 (4) ◽  
pp. 358-370 ◽  
Author(s):  
C. H. Lo ◽  
Y. K. Wong ◽  
A. B. Rad
Keyword(s):  
Fuzzy Logic ◽  
System Performance ◽  
Fuzzy Logic Control ◽  
Controller Design ◽  
Logic Control ◽  
Simulated Environment ◽  
Process Plant ◽  
Computer Aided ◽  
On Line ◽  
Computer Aided Learning

A computer-aided controller design package is developed in this paper. The package provides a simulated environment for simulating the action of a controller under different parameter settings in order to achieve optimal system performance. The designed controller is then applied to a process plant for on-line control.

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