scholarly journals Intelligent Traffic Management Service Using Fuzzy Logic Controller in High Speed Networks

2019 ◽  
pp. 95-98
Author(s):  
Muppineni Sravanthi
Keyword(s):  
Fuzzy Logic ◽  
Traffic Management ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
System Simulation ◽  
Core Area ◽  
Management Service ◽  
High Speed Networks ◽  
Simulation Results

Network traffic management is a core area of research that is of great importance in the field of communication. This paper proposes a new scheme for controlling router side traffic in networks by updating source sending rate according to its IQ size. A new fuzzy controller is to be modelled to implement the proposed system. Simulation results and comparisons has verified the effectiveness and showed that our proposed scheme can achieve better performances than the existing protocols.

CMOS CIRCUIT DESIGN OF A TAKAGI-SUGENO FUZZY LOGIC CONTROLLER

2009 ◽  
Vol 18 (04) ◽  
pp. 841-856
Author(s):  
WEIWEI SHAN ◽  
YAN LIANG ◽  
DONGMING JIN
Keyword(s):  
Fuzzy Logic ◽  
Low Power ◽  
Integrated Circuit ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
High Accuracy ◽  
Real Time Control ◽  
Chip Area ◽  
Takagi Sugeno

This paper presents a low power CMOS analog integrated circuit of a Takagi–Sugeno fuzzy logic controller with voltage/voltage interface, small chip area, relatively high accuracy and medium speed, which is composed of several improved functional blocks. Z-shaped, Gaussian and S-shaped membership function circuits with compact structures are designed, performing well with low power, high speed and small areas. A current minimization circuit is provided with high accuracy and high speed. A follower-aggregation defuzzification block composed of several multipliers for center of gravity (COG) defuzzification is presented without using a division circuit. Based on these blocks, a two-input one-output singleton fuzzy controller with nine rules is designed under a CMOS 0.6 μm standard technology provided by CSMC. HSPICE simulation results show that this controller reaches an accuracy of ±3% with power consumption of only 3.5 mW (at ±2.5 V). The speed of this controller goes up to 0.625M Fuzzy Logic Inference per Second (FLIPS), which is fast enough for real-time control.

A Comparative Analysis of Integrated Boost Flyback Converter using PID and Fuzzy Controller

2015 ◽  
Vol 5 (4) ◽  
pp. 486
Author(s):  
R.Samuel Rajesh Babu
Keyword(s):  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Pid Controller ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Energy System ◽  
Flyback Converter ◽  
Integrated Boost ◽  
Simulation Results

<div class="Section1"><p class="papertitle">This paper presents a comparative analysis of Integrated boost flyback converter for Renewable energy System. IBFC is the combination of boost converter and fly back converter. The proposed converter is simulated in open and closed loop using PID and FUZZY controller. The Fuzzy Logic Controller (FLC) is used reduce the rise time, settling time to almost negligible and try to remove the delay time and inverted response. The performance of IBFC with fuzzy logic controller  is found better instead of PID controller. The simulation results are verified experimentally and  the output of converter is free from ripples and has regulated output voltage.</p></div>

scholarly journals Advanced Traffic Management System using A Fuzzy Logic Controller to Differentiate Parking Vehicle and Moving Vehicle

2020 ◽  
Vol 9 (5) ◽  
pp. 488-494
Keyword(s):  
Fuzzy Logic ◽  
Internet Of Things ◽  
Traffic Management ◽  
Management System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Smart Cities ◽  
Traffic Density ◽  
Moving Vehicle ◽  
Traffic Management System

In smart cities, traffic congestion is one of the significant problems for citizens. Traffic management is an essential one for the quick development of populace and urban movement in metropolitan areas, and traffic blockage is often seeming on streets. To handle different issues for managing traffic on the streets and to help experts in inappropriate arrangement, a smart traffic management system with the IoT (Internet of Things) is proposed in this paper. Mechanisms to utilize IR sensors to distinguish traffic density isn't easy as smooth a solo vehicle recognized at the last sensor so that it can suggest traffic density in high in any event, even if there is free space before it. A technique to be proposed to solve the previously mentioned issues efficiently is by utilizing the Internet of things for traffic management systems. This paper aims to propose a Fuzzy controller to deal with traffics in smart cities. Fuzzy induction used to compute exact traffic, which separates the parking vehicle and moving vehicle. There is an issue of separating parking and un-parking vehicles in the existing systems. So, we planned to solve this using fuzzy logic.

scholarly journals FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

2021 ◽  
Vol 10 (3) ◽  
pp. 195
Author(s):  
Emna Aridhi ◽  
Decebal Popescu ◽  
Abdelkader Mami
Keyword(s):  
Signal Processing ◽  
Fuzzy Logic ◽  
Digital Signal Processing ◽  
Ambient Temperature ◽  
Software Design ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Digital Signal ◽  
Execution Cycle ◽  
Simulation Results

This paper invests in FPGA technology to control the speed of an autonomous car using fuzzy logic. For that purpose, we propose a co-design based on a novel fuzzy controller IP. It was developed using the hardware language VHDL and driven by the Zynq processor through an SDK software design written in C. The proposed IP acts according to the ambient temperature and the presence or absence of an obstacle and its distance from the car. The partitioning of the co-design tasks divides them into hardware and software parts. The simulation results of the fuzzy IP and those of the complete co-design implementation on a Xilinx Zynq board showed the effectiveness of the proposed controller to meet the target constraints and generate suitable PWM signals. The proposed hardware architecture based on 6-LUT blocks uses 11 times fewer logic resources than other previous similar designs. Also, it can be easily updated when new constraints on the system are to be considered, which makes it suitable for many related applications. Fuzzy computing was accelerated thanks to the use of digital signal processing blocks that ensure parallel processing. Indeed, a complete execution cycle takes only 7 us.

Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

2011 ◽  
Vol 403-408 ◽  
pp. 5068-5075
Author(s):  
Fatma Zada ◽  
Shawket K. Guirguis ◽  
Walied M. Sead
Keyword(s):  
Fuzzy Logic ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
System Response ◽  
Robot Arm ◽  
Neural Controller ◽  
Hybrid Controller ◽  
Neural Fuzzy ◽  
Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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.

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