ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR

SINERGI

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Field Value
 
Title ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR
 
Creator Aribowo, Widi
Joko, Joko
Isnur, Subuh
Hermawan, Aditya Chandra
Achmad, Fendi
Nugroho, Yuli Sutoto
 
Subject
Atomic Search Optimization; DC Motor; Metaheuristic; Neural Network; PID;
 
Description DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.
 
Publisher Universitas Mercu Buana
 
Contributor
 
Date 2021-07-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/9680
10.22441/sinergi.2021.3.003
 
Source SINERGI; Vol 25, No 3 (2021); 259-268
24601217
14102331
 
Language eng
 
Relation https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/9680/4788
https://publikasi.mercubuana.ac.id/index.php/sinergi/article/downloadSuppFile/9680/1545
 
Rights Copyright (c) 2021 SINERGI
 

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