Sinergi
Vol 25, No 3 (2021)

ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR

Widi Aribowo (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)
Joko Joko (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)
Subuh Isnur (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)
Aditya Chandra Hermawan (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)
Fendi Achmad (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)
Yuli Sutoto Nugroho (Electrical Engineering Education Department, Faculty of Engineering, Universitas Negeri Surabaya)



Article Info

Publish Date
30 Jul 2021

Abstract

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.

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Journal Info

Abbrev

sinergi

Publisher

Subject

Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Description

SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, ...