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All Journal International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization
Kubota, Naoyuki
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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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.
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing. 
Co-Authors Adi Wibowo Amin Suharjono Anindya Wirasatriya Anom Besari, Adnan Rahmat Ardilla, Fernando Azhar Aulia Saputra Chin, Wei Hong I Ketut Agung Enriko Ismail, Zool H Kurnianingsih Kurnianingsih, Kurnianingsih Lutfan Lazuardi Moh. Yani Rakasiwi, Rizky Khaerul Maulana