Komparasi Metode Optimasi Adam dan SGD dalam Skema Direct Inverse Control untuk Sistem Kendali Data Sikap dan Ketinggian Quadcopter

MUHAMMAD SABILA HAQQI, BENYAMIN KUSUMOPUTRO

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ABSTRAK

Terdapat banyak variable nonlinear dalam sistem kendali untuk quadcopter sehingga cukup rumit untuk mengatur dinamika penerbangan wahana ini. Untuk mengatasi masalah tersebut akan dikembangkan suatu skema sistem kendali Direct Inverse Control menggunakan Deep Learning berbasis Artificial Neural Network (ANN). Sistem yang dikembangkan akan mengendalikan data sikap dan ketinggian quadcopter. Pada artikel ini akan dibandingkan kinerja dari dua metode optimasi untuk Mean Squared Error pada simulasi, yaitu Adaptive Moment Estimation dan Stochastic Gradient Descent. Hasil menunjukkan metode Adaptive Moment Estimation mampu memberikan nilai Mean Squared Error yang lebih kecil dibandingkan metode Stochastic Gradient Descent untuk semua data sikap dan ketinggian yang dikendalikan dengan nilai 0.0069 untuk roll rate, 0.0057 untuk pitch rate, 0.0062 untuk yaw rate, dan 0.0042 untuk data ketinggian.

Kata kunci: Deep Learning, Artificial Neural Network, Adam, SGD, MSE

 

ABSTRACT

There are many nonlinear variables in the control system for the quadcopter so it is quite complicated to regulate the flight dynamics of this vehicle. To overcome this problem, a Direct Inverse Control control system scheme using Deep Learning based on Artificial Neural Network (ANN) will be developed. The system developed will control the attitude and altitude data of the quadcopter. In this article, we will compare the performance of two optimization methods for Mean Squared Error in simulation, namely Adaptive Moment Estimation and Stochastic Gradient Descent. The results show that the Adaptive Moment Estimation method is able to provide a smaller Mean Squared Error value than the Stochastic Gradient Descent method for all attitude and altitude data controlled with values of 0.0069 for roll rate, 0.0057 for pitch rate, 0.0062 for yaw rate, and 0.0042 for altitude data.

Keywords: Deep Learning, Artificial Neural Network, Adam, SGD, MSE


Kata Kunci


Deep Learning; Artificial Neural Network; Adam; SGD; MSE

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i2.458

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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Department of Electrical Engineering Institut Teknologi Nasional Bandung

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Contact: +627272215 (ext. 206)

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