Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

  Widi Aribowo (1*), Supari Muslim (2), Fendi Achmad (3), Aditya Chandra Hermawan (4)

(1) Universitas Negeri Surabaya - Indonesia orcid
(2) Universitas Negeri Surabaya - Indonesia
(3) Universitas Negeri Surabaya - Indonesia
(4) Universitas Negeri Surabaya - Indonesia
(*) Corresponding Author

Received: January 14, 2021; Revised: May 20, 2021
Accepted: July 07, 2021; Published: August 31, 2021

How to cite (IEEE): W. Aribowo, S. Muslim, F. Achmad,  and A. C. Hermawan, "Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor," Jurnal Elektronika dan Telekomunikasi, vol. 21, no. 1, pp. 48-54, Aug. 2021. doi: 10.14203/jet.v21.48-54


This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.



Seagull Optimization Algorithm; metaheuristic; DC motor; neural network

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