scholarly journals IMPLEMENTATION OF FUZZY LOGIC CONTROLLER IN GREEN HOUSE AUTOMATION

2009 ◽  
pp. 11-14
Author(s):  
S.B. Patil ◽  
Dhamakale Sanjay Digambar
Keyword(s):  
Fuzzy Logic ◽  
Logic Programming ◽  
Labor Productivity ◽  
Fuzzy Logic Controller ◽  
Control Methods ◽  
Climate Control ◽  
Matlab Software ◽  
Proportional Control ◽  
Green House ◽  
House Automation

FOR GREEN HOUSE CLIMATE CONTROL, IN NOW DAYS COMPUTERIZED CONTROL IS REQUIRED. MANY TRADITIONAL SYSTEMS ARE BASED ON (1)ON-OFF (2)PROPORTIONAL CONTROL METHODS, WHICH ARE NOT SO EFFECTIVE .DRAWBACKS OF THIS METHODS ARE LOSS OF ENERGY, LABOR ,PRODUCTIVITY.(1)IN ORDER TO MAINTAIN A STEADY CLIMATE A MORE SOPHISTICATED SYSTEM MUST BE USED. BY USING FUZZY LOGIC LOGIC PROGRAMMING GREEN HOUSE CLIMATE CONTROLLER(GHCC) WILL BE DESIGNED. DEVELOPED FUZZY LOGIC CONTROLLER(FLC) IS BASED ON MAMADANI CONTROLLER & IT IS BASED ON MATLAB SOFTWARE . The controller developed effectively controls factors such as temperature &another important parameter, humidity of green house.

scholarly journals Implementation of Bidirectional DC to DC Converter and Inverter for Vehicle to Grid System by using Sliding Mode and Fuzzy Logic Controller

2021 ◽  
Vol 9 (VII) ◽  
pp. 3940-3948
Author(s):  
Jayshree Bembde
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Grid System ◽  
Matlab Software ◽  
Vehicle To Grid ◽  
Grid Applications ◽  
Grid Technique

In this paper a bidirectional DC to DC converter is designed by using sliding mode controller and fuzzy logic controller for vehicle to grid system. The vehicle to grid technique is intended to work in both grid to a vehicle battery and vehicle battery to grid applications in vehicle-to-grid scenarios. The two methodologies is verified using MATLAB software. Hardware is made by using a LAUNCHXL-F28377S microcontroller.

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.

scholarly journals Improved fuzzy logic controller for cane juice extraction

2021 ◽  
Vol 2070 (1) ◽  
pp. 012132
Author(s):  
A Vasanthi ◽  
Yogesh Misra ◽  
P V Murali Krishna
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Sugar Cane ◽  
Fuzzy Logic Controller ◽  
Sugarcane Juice ◽  
System Control ◽  
Matlab Software ◽  
Cane Juice ◽  
Juice Extraction

Abstract In current days Fuzzy logic plays auspicious role to provide so many choices for many applications which are system control. Fuzzy logic having no. of practical methodologies in soft computing. Sugar making industries are one of the applications using fuzzy logic controller. sugarcane juice extraction is an industry which is highly seasonable. If the supply of sugar cane is irregular then that reduces the efficiency of extractor from cane to mass producing sugar. So, it is necessary to improve the efficiency of extractor, which gives juice from cane billets. For that we must keep up the Donnelly height (or) level of cane is closed to 90cm. Where a methodology introduced to improve the three inputs controller with fuzzy is used to keep up the cane volume (or) level of Donnelly channel. For that cane level must and should always at 90cm. To limited that level (or) height of cane in chute (or) channel was developed by using of tool box in fuzzy logic designer, MATLAB software.

Fuzzy Logic Controller for Truck Active Suspension and Related Optimal Control Methods

2013 ◽  
Vol 441 ◽  
pp. 821-824 ◽  
Author(s):  
Gong Yu Pan ◽  
Teng Yue Xu ◽  
Yun Chen
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Complex Structure ◽  
Active Suspension ◽  
Control Methods ◽  
Front Suspension ◽  
Suspension Model ◽  
The Cost ◽  
Vibration Performance ◽  
Optimal Control Methods

A 5-DOF active suspension model is established to study the trucks vibration performance, and a fuzzy logic controller has been designed in Matlab/Simulink. The simulation result shows that the control system on the trucks body suspensions can not only reduce the vibration of body but also the vibration level of the drivers seat, which improves the maneuverability and stability of the truck. Considering that the active suspension has complex structure and high cost, it is necessary to find a way to reduce the cost of production. The independent active control on front suspension or rear suspension has been studied and the effectiveness of the different control methods have been compared in detail.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

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