A RISC microcontroller based voltage regulator module with fuzzy logic controller for processor core in mobile systems

2012 ◽  
Author(s):  
Monaf S. Tapou ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Mobile Systems ◽  
Voltage Regulator ◽  
Processor Core

scholarly journals Prognostic Methods on Accelerator’s Anode Voltage Regulator

2018 ◽  
Vol 43 ◽  
pp. 01020
Author(s):  
Robby Christian ◽  
Balza Achmad ◽  
Hyun Gook Kang
Keyword(s):  
Fuzzy Logic ◽  
Random Forest ◽  
Fuzzy Logic Controller ◽  
Failure Modes ◽  
Anode Voltage ◽  
Fault Classification ◽  
Voltage Regulator ◽  
Fault Diagnostics ◽  
Effect Analysis ◽  
Progression Model

This study investigated an adaptive control, fault diagnostics and prognostics of the anode voltage regulator system at an ion implantation accelerator. The system was modeled as a 4th order AutoRegressive with eXogenous (ARX) model, controlled by a Fuzzy Logic Controller (FLC). This model was then used as a basis for constructing and updating a fault diagnosis module and a failure prognostics module. To maintain the system’s performance, the controller’s response was continuously re-adjusted through an optimization scheme. A Failure Mode and Effect Analysis (FMEA) was conducted resulting on five failure modes of the regulator system. Fault data were generated in MATLAB simulation to train a random forest fault classification engine. The optimal random forest classifier was 20 decision trees with a fault diagnostics accuracy of 98.06%. A Hidden Markov Model (HMM) was constructed as the system’s fault progression model based on the interaction between environmental conditions and controller actions. The particle filter and Bayesian inference methods were then employed to continuously update the HMM and predict the system’s Remaining Useful Lifetime (RUL). The proposed methodology was able to integrate an adaptive fuzzy logic control, prognosis and failure diagnosis altogether allowing a continual satisfactory performance of the voltage regulator system throughout its lifetime.

scholarly journals PI Fuzzy Controller of Synchronous Boost Converter for Drug Storage Thermoelectric Cooler

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Alvin Noer Ramadhan ◽  
Novie Ayub Windarko ◽  
Irianto Irianto
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Pi Controller ◽  
Boost Converter ◽  
Settling Time ◽  
Voltage Regulator ◽  
Set Point ◽  
Drug Storage

Medicines should be stored in a room at a suitable temperature if the inappropriate affect the quality of the drug. Therefore we need a control that can control the temperature in the room so that it is constant in accordance with the rules for room temperature in drug storage, which is 25 degrees Celsius. The following paperwork presents a simulation controller between PI controller and PI-Fuzzy logic controller in adjusting the voltage to match the set of point. Where the fuzzy logic controller automatically searches for the Kp value so that the voltage output of the converter match the desired set of point. Then the converter used is synchronoust boost converter as voltage regulator and peltier as a DC load which functions as a cooler. in this research, the system using  PI controller was able to adjust the voltage to match the set point with Kp is 0.14089 and Ki is 124.6738 then settling time is 0.016 s. While the system using PI-Fuzzy logic controller,it was able to adjust the voltage to match the set point with Kp is 0.08112 and Ki is 125.6738 then settling time is 0.014 s.

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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