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All Journal Program Kreativitas Mahasiswa - Teknologi JOIV : International Journal on Informatics Visualization
Azhar Aulia Saputra
Jurusan Teknik Elektronika, Fakultas Elektronika, Politeknik Elektronika Negeri Surabaya
Computer Science & IT Engineering

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SISTEM MONITORING DAN SISTEM PENYEIMBANG BERAT MUATAN KAPAL FERRY SEBAGAI ANTISIPASI KECELAKAAN Saputra, Azhar Aulia; Mardianto, Endi; Adikoro, Ahmad Harits
Program Kreativitas Mahasiswa - Teknologi PKM-T 2013
Publisher : Ditlitabmas, Ditjen DIKTI, Kemdikbud RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.985 KB)

Abstract

Shipwreck is influenced by several factors, namely the ship and the port system is less than optimal, assuming good weather conditions. Invention at the port is to improve the performance of the port in knowing overweight and balance boatload of ships so as to avoid the possibility of accidents due to problems on the ship and the port. System improvements in the detection port charge balance on the ship using imu acellero Arduino board to detect the slope. Lab scale testing condition for sending and receiving data between a boatload of weight and handheld hardware android.Pengiriman angle of the data on the microcontroller via bluetooth at a distance of 30 meters with an average error 0.5%. Data on the equilibrium angle detection using imu acellero Arduino board has an average error 2.0%.
Optimizing Quadrotor Stability: RBF Neural Network Control with Performance Bound for Center of Gravity Uncertainty Yani, Mohamad; Ardilla, Fernando; Anom Besari, Adnan Rahmat; Saputra, Azhar Aulia; Kubota, Naoyuki; Ismail, Zool H
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2918

Abstract

The Radial Basis Function (RBF) neural network has been widely applied for approximating nonlinear systems and improving control robustness, particularly in uncertain conditions such as dynamic shifts in the quadrotor’s Center of Gravity (COG). However, initial weight estimation errors can degrade transient responses, reducing tracking performance. This study proposes a novel RBF-based control scheme integrated with a performance-bound mechanism to enhance quadrotor stability under COG uncertainty. The performance bound ensures that the quadrotor’s motion remains within a defined region around the reference trajectory, thereby minimizing steady-state and transient errors. The RBF network is trained online to estimate the system’s dynamic changes, and the controller is designed using a Lyapunov-like function to ensure stability. Simulation results show that the proposed controller achieves better tracking accuracy and significantly lower energy usage, with total force and moment values reduced compared to the standard RBF controller. Specifically, the proposed controller uses 3010.7 N of force and 2.2427 Nm of moment, while the standard controller requires 3150.2 N and 15.197 Nm. These results confirm that the proposed method provides improved performance and energy efficiency. This research highlights the potential of integrating performance bounds in neural network control for robust quadrotor navigation. Future work includes real-world experiments to validate performance under varying COG perturbations.
Co-Authors Ahmad Harits Adikoro Anom Besari, Adnan Rahmat Ardilla, Fernando Endi Mardianto Ismail, Zool H Kubota, Naoyuki Moh. Yani