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All Journal Jurnal Elektronika dan Telekomunikasi Indonesian Journal of Electrical Engineering and Computer Science
Anggraeni, Sakinah Puspa
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Electrical & Electronics Engineering Engineering

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Performance Comparison of PID, FOPID, and NN-PID Controller for AUV Steering Problem Nami, Osen Fili; Widaryanto, Afif; Rasuanta, Muhammad Putra; Pramudya, Tinova; Firdaus, Muhammad Yusha; Widati, Peni Laksmita; Anggraeni, Sakinah Puspa; Dwiyanti, Hanifah; Rahmadiansyah, Maristya; Purwoadi, Michael Andreas; Rahardjo, Sasono; Lubis, Teddy Alhady
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.596

Abstract

This study examines and compares three Autonomous Underwater Vehicles (AUV) steering control techniques utilizing the following three control algorithms: Proportional-Integral-Derivative (PID), Fractional Order PID (FOIPD), and Neural Network-PID (NN-PID). The objective of this investigation is to gain a comprehensive understanding of each controller's response in terms of step input scenarios, trajectory changes, and when encountering disturbances. The response analysis will evaluate the strengths and weaknesses of the controller by examining parameters such as Rise Time, Settling Time, Settling Min, Settling Max, Overshoot, Peak, and Peak Time for each controller response. To determine the accuracy performance of each controller strategy, the root mean square error (RMSE) technique will be applied, allowing users to confidently select the most suitable controller option. FOPID displays the best settling time of 3.2218 seconds, and PID stands out in rise time, achieving 0.4725 seconds. The results indicate that NN-PID is the top performer as it reduces overshoot to 0.3022%. Among the three controllers that were tested, FOPID had the smallest RMSE value, while the NN-PID control's slower response and larger error resulted in a smaller overshoot than PID and FOPID. This factor is due to the online learning process on NN-PID, which requires time. Based on the simulation results, FOPID outperforms PID in settling time and produces the smallest error due to the inclusion of parameters λ and μ, leading to improved control performance.
BFT water color classification in tilapia aquaculture using computer vision Suwandi, Bondan; Anggraeni, Sakinah Puspa; Palokoto, Toto Bachtiar; Sulistya, Budi; Sujatmiko, Wisnu; Septiawan, Reza; Taufik, Nashrullah; Rufiyanto, Arief; Ardiansyah, Arif Rahmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp497-508

Abstract

Biofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results.
Co-Authors Ardiansyah, Arif Rahmat Dwiyanti, Hanifah Firdaus, Muhammad Yusha Lubis, Teddy Alhady Nami, Osen Fili Palokoto, Toto Bachtiar Pramudya, Tinova Purwoadi, Michael Andreas Rahmadiansyah, Maristya Rasuanta, Muhammad Putra Rufiyanto, Arief Sasono Rahardjo, Sasono Septiawan, Reza Sujatmiko, Wisnu Sulistya, Budi Suwandi, Bondan Taufik, Nashrullah Widaryanto, Afif Widati, Peni Laksmita