Implementation of Fuzzy Logic Control in Semiactive Suspension for a Vehicle Using MATLAB SIMULINK

2016 ◽  
Author(s):  
Shraddhesh Rasal ◽  
Jayanth Jaganmohan ◽  
Sohan Agashe ◽  
Kiran P Wani
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Matlab Simulink ◽  
Logic Control ◽  
Semiactive Suspension

Fuzzy Logic Controller Implementation on Separately Excited DC-Motor

2019 ◽  
Vol 2 (3) ◽  
pp. 134-143
Author(s):  
Ajisman Apen ◽  
Ker Shao Jiun ◽  
Siti Nursyuhada Mahsahirun
Keyword(s):  
Fuzzy Logic ◽  
Motor Control ◽  
Linear System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Matlab Simulink ◽  
System Parameters ◽  
Logic Control ◽  
Non Linear System

Various industrial and domestic applications such as automotive, aerospace,appliances and many others are electrically driven. Conventional methods of motor control had failed to produce desired performance of DC-motor due the system parameters variation and load changes. The Fuzzy-Logic-controller is one of the controllers that can handle non-linear system. This project is aimed to control the speed of a separately-excited DC-motor using fuzzy logic control. The system is simulated on MATLAB-Simulink and implemented on ARDUINO Uno development board.

Studying of Fuzzy Logic Control Semi-Active Suspension Based on ADAMS/Car and MATLAB/Simulink

2010 ◽  
Vol 143-144 ◽  
pp. 956-960 ◽  
Author(s):  
Jing Jun Zhang ◽  
Zhi Qiang Xu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Active Suspension ◽  
Matlab Simulink ◽  
Logic Control ◽  
Car Suspension ◽  
Genetic Algorithm Approach ◽  
Multi Body ◽  
Body Dynamic

The study is researched to establish a multi-body dynamic model of the Vehicle by using Adams /Car software package. Using genetic algorithm approach，fuzzy logic controller of the semi-active suspension was designed, simulated in the environment of Matlab/Simulink.. The results of being compared with the passive suspension demonstrate that this developed fuzzy logic controller based on Adams /Car and Matlab/Simulink enhances the performance of the full car suspension system significantly.

scholarly journals Single Axis Tracking PV Panel Using Fuzzy Logic Control

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Pandu Sudibyo ◽  
Yanu Shalahuddin ◽  
Mochtar Yahya
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Matlab Simulink ◽  
Pv Panel ◽  
Logic Control ◽  
Solar Tracker

Abstrak – Panel PV(Photovoltaic) merupakan teknologi yang mengubah energi cahaya matahari menjadi energi listrik. Maka dari itu untuk mendapatkan iradiansi maksinal perlu sistem solar tracker sebagai cara untuk optimalisasi penyerapan cahaya matahari. Pada penelitian ini membahas pembuatan model simulink solar tracker menggunakan kontroler fuzzy logic. Arah sinar matahari disensor mengguanakan 2 buah sensor LDR (Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Sistem terdiri atas 4 komponen utama yaitu PV Modul ,Mikrokontroler, motor servo, sensor LDR(Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Output logika fuzy berupa nilai yang kemudian diumpan ke servo untuk gerakan panel secara Single Axis. Aplikasi Matlab Simulink sebagai compiler dan pembuat permodelan sistem yang nantinya akan diupload ke mikrokontroler. Arah putaran motor servo ditentukan dengan menggunakan kendali logika fuzzy.  Hasil pengujian membuktikan rata-rata tegangan panel PV lebih tinggi daripada panel tanpa kendali, dengan nilai rata-rata sebesar 14,35V.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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