Journal article // SINERGI






ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR
October 2021  //  DOI: 10.22441/sinergi.2021.3.003
Widi Aribowo, Joko Joko, Subuh Isnur, Aditya Chandra Hermawan

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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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