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All Journal INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Irvan Malay
Universitas Pembangunan Panca Budi, Indonesia
Religion Humanities Social Sciences

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A COMPARATIVE STUDY OF PHOTOVOLTAIC MAXIUM POWER POINT TRACKING ALGORITHMS UNDER DYNAMIC WEATHER CONDITIONS Irvan Malay; Dimas Zakyla Akbar; Kinaya Arindra; Fahryn Al Hafiz; Nada Qirania Sakila; Syahril Qadar Karo Karo; Muhammad Habib; Triantono Simarmata
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 2 No. 10 (2025): AUGUST
Publisher : Adisam Publisher

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Based on a literature review of various MPPT algorithms, it can be concluded that each algorithm has its own advantages and limitations depending on the operational conditions of the photovoltaic system. Conventional algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC) offer a simple structure and easy implementation, but are less responsive to rapid weather changes. Meanwhile, artificial intelligence-based algorithms such as Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), and Particle Swarm Optimization (PSO) demonstrate superior performance in terms of tracking speed, efficiency, and stability under dynamic conditions. The combination of algorithms or hybrid methods has also been proven to improve system resilience to irradiance and temperature fluctuations. Therefore, the selection of an MPPT algorithm must consider the context of use, such as environmental conditions, hardware capacity, and the overall efficiency needs of the system. With the right approach, MPPT systems can significantly increase the power output of solar panels and support sustainable energy efficiency.
ADAPTIVE CONTROL STRATEGIES FOR ROBOTIC MANIPULATORS USING DEEP REINFORCEMENT LEARNING Irvan Malay; Muhammad Alfa Rozi; Ahmad Dzaky; Afif Arroofi; Mitgever Pandiangan; Haikal Lutfian Hsb; Febrio Ardly Saefsan
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 2 No. 10 (2025): AUGUST
Publisher : Adisam Publisher

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This research examines adaptive control strategies for robotic manipulators using Deep Reinforcement Learning (DRL) through a systematic literature review approach. The main focus of the study is the identification of commonly used DRL algorithms, implementation challenges, and the direction of developing DRL-based adaptive control systems. The study results show that algorithms such as DDPG, SAC, and PPO are effective in addressing the non-linear dynamics and uncertainties of robotic manipulators, both in simulation and real-world environments. However, there are significant challenges such as the need for large training data, the simulation-to-real gap, and the limitations in the interpretability of control policies. The integration of hybrid control strategies, the development of more sample-efficient algorithms, and the application of hierarchical and meta-reinforcement learning have been identified as promising future research directions. This study provides a foundation for the development of more flexible, efficient, and safe robotic control systems to support various industrial applications.
Co-Authors Afif Arroofi Ahmad Dzaky Dimas Zakyla Akbar Fahryn Al Hafiz Febrio Ardly Saefsan Haikal Lutfian Hsb Kinaya Arindra Mitgever Pandiangan Muhammad Alfa Rozi Muhammad Habib Nada Qirania Sakila Syahril Qadar Karo Karo Triantono Simarmata