scholarly journals Positioning control of square arrangements of solar panels by solar tracing using fuzzy logic

2021 ◽  
Vol 2 (2) ◽  
pp. 3318-3331
Author(s):  
Diego Armando Mejía ◽  
Ivaldo Torres Chávez ◽  
Abelardo Mejía
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Static System ◽  
Maximum Point ◽  
Control Loop ◽  
Solar Panels ◽  
Positioning Control ◽  
Prototype Development ◽  
Radiation Sensors

In this project a fuzzy control system is developed to follow the sun with its respective dynamics.  The positioning of the panel must take into account changes in the weather, the disturbances that may occur, the area in which the tracker is located and so on.  To do this, the maximum capture point positioning control system is studied using fuzzy logic.  Prior to design and implementation, corresponding design criteria were established in instrumentation, mechanical structure, control, communication, and processing for prototype development.  By means of the above, the photovoltaic array positioning system is designed, to then carry out the efficient implementation thanks to the calculations and adjustments made.  After having the physical structure, we start to develop the positioning control of quadruple arrangements of solar panels with two degrees of freedom per maximum point of capture of solar radiation using fuzzy control, taking into account that the radiation sensors give a reference point. for the setpoint in the control loop and after this, control signals are sent to the selected actuators.  Finally, the control system is validated for the positioning of the tracker with the fuzzy control loop implemented; In addition, the system is compared with the strategy implemented before a static system in order to analyze its efficiency.  In order for the solar tracking system to show greater efficiency compared to the static system, radiation sensors are implemented that compensate for the influence of climatic disturbances that any solar panel tends to suffer.

Permanent Magnet Chatter Absorber with Fuzzy Logic Control

2009 ◽  
Vol 147-149 ◽  
pp. 290-295 ◽  
Author(s):  
Bogdan Broel-Plater ◽  
Stefan Domek ◽  
Arkadiusz Parus
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Control Algorithm ◽  
Permanent Magnets ◽  
Practical Experience ◽  
High Energy ◽  
Logic Control ◽  
Machine Operators

The paper deals with semi-active chatter absorber based on an electrodynamic transducer built around high-energy permanent magnets. Also, a fuzzy logic control system for the absorber control system has been designed. The principal advantage of fuzzy control is the possibility to implement practical experience gained by machine operators in the control algorithm. Hence, the possibility of factoring such quantities, as vibrations experienced by selected points of the machine-tool, and sound emitted by working machine into the analyzed chatter absorber fuzzy control system has been studied in the paper. The control system has been tested by way of simulation with the use of the process and cutting force models.

The Application of MATLAB for Fuzzy Control System Simulation

2014 ◽  
Vol 494-495 ◽  
pp. 1306-1309 ◽  
Author(s):  
Xian Qiu Xu
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
System Simulation ◽  
Paper Briefly ◽  
C Language ◽  
Fuzzy Control System ◽  
Function Module

The design of fuzzy control system is one of the important problems of the application of fuzzy control system. And the system simulation is an important step in the design of and the necessary guarantee. This paper briefly introduces the MATLAB language, the fuzzy logic toolbox, and SILMULINK toolbox. Then the examples, this method overcome the traditional in c language simulation of complex and inconvenient shortcoming. It is easy to fuzzy control system simulation, direct and fast. Finally also introduced several methods of create SILMULINK user s function module.

scholarly journals A Philosophical Treatise and Educational Aspects on Fuzzy Control

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Human Activity ◽  
Input Data ◽  
Feedback Controller ◽  
Central Heating ◽  
Almost All ◽  
Heating Boiler

Fuzzy Logic has found nowadays many applications to almost all sectors of human activity, withfuzzy control being one of the most important such applications. A control system regulates the behavior of adevice or another system with the help of a feedback controller. A fuzzy control system is a control system thatanalyses the input data in terms of variables which take continuous values in the interval [0, 1]. The presentarticle studies in detail the operation of fuzzy control systems and illustrates it by presenting an exampleof controlling a building’s central heating boiler.

Some Recent Defuzzification Methods

2017 ◽  
pp. 31-48 ◽  
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

Some Recent Defuzzification Methods

Fuzzy Systems ◽  
2017 ◽  
pp. 1003-1019
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

Research on Fuzzy Control System for Air-Condition in Bus

2012 ◽  
Vol 457-458 ◽  
pp. 998-1001
Author(s):  
Qiu Hua Miao ◽  
Pei Gang Jiao ◽  
Jie Liang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Temperature Signal ◽  
Working Principle ◽  
Fuzzy Logic Technique ◽  
Deflection Rate

In this paper fuzzy logic control is introduced in auto adjust control system of air in bus. Fuzzy controller employs fuzzy control model of two inputs and one output. The paper also introduces the hardware framework, working principle and software of the system, analyzing merits of fuzzy logic technique, putting emphasis on the design of the fuzzy controller. In the controlling process, fuzzy controller samples temperature signal in bus and calculate deflection and deflection rate of temperature, looks up corresponding output in controlling table, and calculates its precision, then fuzzy controller gives related signal to motors and valve, acquiring satisfactory effect.

Simulation for Temperature Control System of Dyeing Machine Based on Fuzzy Control

2011 ◽  
Vol 317-319 ◽  
pp. 1688-1692
Author(s):  
Min Ling Zhao ◽  
Guo Ping Li ◽  
Xiong Bo Ze ◽  
Cheng Kai Ji
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Decisive Role ◽  
Temperature Control System ◽  
Logic Control

In the process of dyeing, the temperature control of dyeing machine plays a decisive role on the stand or fall quality of fabric. The establishment of the traditional PID controller’s parameters needs a lot of test, which brings many inconvenience.Therefore, it is proposed to control dyeing machine temperature by fuzzy controller. Based on the principle of fuzzy logic control, the model of the temperature control system of dyeing machine is built. At the same time, through the fuzzy logic toolbox in matlab software, fuzzy controller of temperature is designed. Then a comparative simulation of the temperature control system of dyeing machine with matlab has been accomplished. Through the analysis of the results, it is concluded that the temperature system can achieve the higher steady precision.

scholarly journals Fuzzy control of quadrotor Ar. Drone 2.0 in a controlled environment

2019 ◽  
Vol 13 (1) ◽  
pp. 39-49
Author(s):  
Fernando Campos Archila ◽  
Valentina Pinzón Saavedra ◽  
Faiber Robayo Betancourt
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Controlled Environment ◽  
The Real ◽  
Height Control ◽  
Battery Charge ◽  
Design And Implementation ◽  
Reference Parameters ◽  
Ar Drone

This paper aims to describe the design and implementation of the height control system for the quadrotor AR. Drone 2.0 making use of a fuzzy logic in a previously established environment. This device has a height control system both in simulation and in the real platform. Three controllers are developed by fuzzy logic whose parameters are obtained from the drone's sensors in such a way that it allows to control height and angles of orientation (Pitch, Roll and Yaw) as long as certain levels of battery charge are considered so that the system does not become unstable. For the visualization and interaction with the drone a Matlab® interface is designed and implemented, that allows communication between the user and all system functions in such a way that the mode of execution can be chosen, follow the reference parameters autonomously, store data for a later analysis, and visualize the displacements to observe the efficiency of system.

scholarly journals Modeling of automatic control system for air supply to a bio-reactor using fuzzy control

2021 ◽  
Vol 2131 (2) ◽  
pp. 022009
Author(s):  
V F Lubentsov ◽  
E A Shakhrai ◽  
E V Lubentsova
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Automatic Control ◽  
Automatic Control System ◽  
Fuzzy Controller ◽  
Nonlinear Controller ◽  
Steady State Mode ◽  
Air Supply

Abstract The stages of modeling the automatic control system (ACS) for air supply to aeration with the use of fuzzy control are considered. The investigated control algorithm is based on the combination of a nonlinear controller with approximating control (CAC), whose parameters are corrected using fuzzy logic. The algorithm for correcting the CAC parameters for transient and steady state modes is based on the application of two simple rulebases (RB) with three and five linguistic terms, respectively. As a result, the required speed in the transient mode and accuracy in the steady state mode are provided. It is proved that switching the RB according to the logic of the multi-mode system is less demanding on the number of rules, structure and setting parameters of the membership function than using the extended RB. The differences between the proposed ACS with different BP for the main operating modes of the system are shown. These include: improvement of quality indicators due to the implementation of different BP in different modes; more rigorous justification of the mechanism for ensuring insensitivity to the switching moments of BP when changing modes due to the CAC of the direct circuit of the ACS. Effective implementation of the stages of ACS modeling and fuzzy controller design is possible using the Fuzzy Logic Toolbox system of the Simulink MATLAB modeling environment.

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