scholarly journals Fuzzy Regulator Design for Wind Turbine Yaw Control

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Stefanos Theodoropoulos ◽  
Dionisis Kandris ◽  
Maria Samarakou ◽  
Grigorios Koulouras
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Automatic Control ◽  
Wind Turbine ◽  
Power Output ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Yaw Control ◽  
Fuzzy Regulator ◽  
System Effectiveness

This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.

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 Online Position Control Performance Improving Applying Incremental Fuzzy Logic Controller

2018 ◽  
Vol 248 ◽  
pp. 02005
Author(s):  
Dirman Hanafi ◽  
Mohamed Najib Ribuan ◽  
Wan HamidahWan Abas ◽  
Hidayat ◽  
Elmy Johana ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Motor Performance ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Peak Time ◽  
Control Performance ◽  
Position Control System

This paper presents the online control system application for improving the DC motor performance. DC motor widely used in industries and many appliances. For this aim fuzzy logic controller is applied. The type of fuzzy controller use is an incremental fuzzy logic controller (IFLC). The IFLC is developed by using MATLAB Simulink Software and implemented in online position control system applying RAPCON board as a platform. The experimental results produced the best gains of the IFLC are 1.785, 0.0056955 and 0.01 for error gain (GE), gain of change error (GCE) and gain of output (GCU) respectively. Its produce smaller rise time, peak time, 0% overshoot and smaller settling time. Beside that the IFLC response also able to follow the set point. The controller response parameters values are also acceptable. It means that the IFLC suitable to be use for improving the position control system performance.

Intelligent Control System Design for a Teleoperated Endoscopic Surgical Robot

2015 ◽  
Vol 789-790 ◽  
pp. 693-699
Author(s):  
Alaa Khalifa ◽  
Ahmed Ramadan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
System Design ◽  
Trajectory Tracking ◽  
Parallel Manipulator ◽  
Control Algorithm ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Control System Design ◽  
Matlab Simulink

This paper concerns with the control system design for a teleoperated endoscopic surgical manipulator system that uses PHANTOM Omni haptic device as the master and a 4-DOF parallel manipulator (2-PUU_2-PUS) as the slave. PID control algorithm was used to achieve the trajectory tracking, but the error in each actuated joint reached 0.6 mm which is not satisfactory in surgical application. The design of a control algorithm for achieving high trajectory tracking is needed. Simulation on the virtual prototype of the 4-DOF parallel manipulator has been achieved by combining MATLAB/Simulink with ADAMS. Fuzzy logic controller is designed and tested using the interface between ADAMS and MATLAB/Simulink. Signal constraint block adjusted the controller parameters for each actuated prismatic joint to eliminate the overshoot in most of position responses. The simulation results illustrate that the fuzzy logic control algorithm can achieve high trajectory tracking. Also, they show that the fuzzy controller has reduced the error by approximately 50 percent.

scholarly journals Automatic removal of the plane from a spin using fuzzy logic controller

2019 ◽  
Vol 2019 (2) ◽  
pp. 45-53
Author(s):  
Jacek Prusik ◽  
Tomasz Rogalski
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Angular Velocity ◽  
Automatic Control ◽  
Automatic Control System ◽  
Fuzzy Logic Controller ◽  
Flight Simulator ◽  
Control Algorithms ◽  
Flight Parameters ◽  
Automatic Removal

The paper presents a concept of automatic control system recovering an aircraft from the spin using fuzzy logic controller. Control system causing: stall, spin, spin recovery, dive recovery and switching on classic heading and altitude autopilots, was created in Matlab – Simulink software, which was connected to the flight simulator X-Plane. During tests developed control algorithms were checked and tuned. At the end graphs of flight parameters recorded during simulation were analyzed, and properties of designed control system were evaluated. Particular attention was paid to the design of a fuzzy logic controller stopping autorotation of the aircraft. On the output it controlled the position of the rudder, while on input it received a signal being a function of the angular velocity of the aircraft.

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.

scholarly journals Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Oil And Gas ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

scholarly journals Desain Model Fuzzy Control UAV Berbasis Matlab/Simulink pada Arduino Flight Controller

2018 ◽  
Vol 2 (2) ◽  
pp. 19
Author(s):  
Muchamad Malik ◽  
Aan Burhanuddin
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
The Past ◽  
Aerial Vehicle ◽  
Hardware In Loop ◽  
System Designs ◽  
Vehicle Platform

<p><em>Quadrocopter is an aerial vehicle platform that has become very popular among researchers from the past because it has advantages compared to conventional helicopters. The quadrocopter design is very simple and unique but seen from an unstable aerodynamic standpoint. From existing research, researchers have proposed many control system designs for quadrocopter. In this study, the author presents a fuzzy logic controller for quadrocopter. The method in this research is by designing hardware. After that the design for fuzzy controllers. Then the designed fuzzy controller is tested in the Hardware In Loop (HIL) setting. The experimental results and validation of the controller application functions are considered satisfactory and it is concluded that it is possible to stabilize quadrocopter with fuzzy logic controller.</em></p>

scholarly journals ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Manuel Braz César ◽  
Rui Carneiro Barros
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Magnetorheological Dampers ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Degrees Of Freedom

Abstract In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

scholarly journals Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Salisu Muhammad Sani
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Problem Solving ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Membership ◽  
Linguistic Variables ◽  
Membership Functions ◽  
Fuzzy Membership Functions

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

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