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Alfian Ma'arif
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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 15 Documents
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Search results for , issue "Vol 2, No 2 (2024)" : 15 Documents clear
Benchmark Analysis of Sampling Methods for RRT Path Planning Pratama, Gilang Nugraha Putu; Dhewa, Oktaf Agni; Priambodo, Ardy Seto; Baktiar, Faris Yusuf; Prasetyo, Rizky Hidayat; Jati, Mentari Putri; Hidayatulloh, Indra
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.132

Abstract

Path planning is a crucial aspect of mobile robot navigation, ensuring that robots can safely travel from their initial position to the goal. In real-world applications, path planning is essential for autonomous vehicles, drones, warehouse robots, and rescue robots to navigate complex environments efficiently and safely. One effective method for path planning is the Rapidly-exploring Random Tree (RRT) algorithm, which is particularly practical in maze-like environments. The performance of RRT depends on the sampling methods used to explore the maze. Sampling methods are important because they determine how the algorithm explores the search space, affecting the efficiency and success of finding an optimal path. Poor sampling can lead to suboptimal or infeasible paths. In this study, we investigate different sampling strategies for RRT, specifically focusing on uniform sampling, Gaussian sampling, and the Motion Planning Network (MPNet) sampling. MPNet leverages a neural network trained on past environments, allowing it to predict promising regions of the search space quickly, unlike traditional methods like RRT that rely on random exploration without prior knowledge. This makes MPNet much faster and more efficient, especially in complex or high-dimensional spaces. Through a benchmarking analysis, we compare these methods in terms of their effectiveness in generating feasible paths. The results indicate that while all three methods are effective, MPNet sampling outperforms uniform and Gaussian sampling, particularly in terms of path length. The mean path length generated, based on a sample size of 30, is 13.115 meters for MPNet, which is shorter compared to uniform and Gaussian sampling, which are 18.27 meters and 18.088 meters, respectively. These findings highlight the potential to enhance path planning algorithms using learning-based sampling methods.
Investigations on Grid-Connected DFIWGs Development and Performance Analysis with the Support of Crowbar and STATCOM Mahmoud, Mohamed Metwally; Benlaloui, Idriss; Benbouya, Basma; Ibrahim, Nagwa F.
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.109

Abstract

These days, one of the most used layouts in the wind power industry is a variable-speed doubly-fed induction wind generator (DFIWG). To provide real and reactive power (PQ) control during grid failures, this research examines the DFIWG. The system's transient behavior is examined under normal and abnormal conditions. Through rotor side converter (RSC) and grid side converter (GSC) control, Q assistance for the grid, and power converter stress reduction, the suggested control approach achieves system stability while enabling DFIWG to operate smoothly during grid failures. By suppressing rotor and stator overcurrent, DC link voltage (VDC) overshoot, and PQ oscillations, as well as supporting the grid voltage (GV) under both balanced and unbalanced grid fault scenarios with distinct voltage dips, the suggested technique preserves the system characteristics during grid faults. MATLAB/SIMULINK 2017b is used for time-domain computer simulations. STATCOM and crowbar, two suggested systems, are contrasted. This work proves the effectiveness of the suggested approaches in augmenting the system's fault ride-through (FRT) capacity.
A Comprehensive Study of Effects of Renewable Energy Based Electric Vehicles on Environment Hossain, Md. Taufiq; Khan, Saidul Islam; Dodaev, Zohar Al
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.100

Abstract

This extensive study explores the environmental impact of electric vehicles (EVs) powered by renewable energy sources. This review also looks at how they might lessen air pollution, cut carbon emissions, and support environmentally friendly transportation networks. Renewable energy-powered electric cars (EVs) have become a viable substitute for conventional internal combustion engine automobiles as the globe moves toward greener energy sources and looks for ways to tackle climate change. This essay examines how adopting EVs would affect the environment, with particular attention to resource use, air quality gains, and greenhouse gas emissions. It examines how EVs' life cycle emissions compare to those of conventional cars, taking into account things like how cars are made, how electricity is produced, and how end-of-life disposal is handled. The study also looks at how renewable energy sources like solar, wind, and hydropower can be integrated into the electrical grid to power electric vehicles (EVs), emphasizing the mutually beneficial effects on the energy and transportation sectors. The study also addresses the possible opportunities and problems that come with the widespread use of EVs powered by renewable energy, including infrastructural needs, legislative incentives, and customer behavior. This study aims to provide important insights into the environmental implications of EVs powered by renewable energy, guiding decision-making processes, and shaping future strategies for sustainable transportation and energy transitions through a thorough analysis of the body of existing literature, empirical studies, and modeling approaches.
A Comprehensive Review of Intelligent Home Automation Systems Using Embedded Devices and IoT Tareq, Abdulla Al; Mostofa, Md Riad; Rana, Md Juel; Rahman, Md Sadiqur
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.110

Abstract

The integration of embedded devices and Internet of Things (IoT) technologies is the main subject of this thorough assessment, which examines the development and status of intelligent home automation systems. Intelligent home automation systems provide remote control and automation of household appliances and systems, with the goal of improving the comfort, safety, and energy efficiency of residential surroundings. The different designs and parts of home automation systems such as sensors, actuators, controllers, communication protocols, and user interfaces are examined in this overview. It draws attention to the function of embedded devices, which act as the essential building blocks of these systems by supplying the required connectivity and processing power. The evaluation also covers the use of IoT technologies, which enable smooth device interoperability and communication, opening the door to more advanced automation and control capabilities. Important developments in artificial intelligence, cloud computing, and machine learning that enhance these systems' intelligence and flexibility are also examined. The paper also discusses issues including security, privacy, standards, and user adoption and offers possible fixes as well as future research possibilities. Creating AI algorithms that will help home automation systems comprehend and react to user context and preferences more effectively which can be done soon. We hope that this review will give readers a thorough grasp of intelligent home automation systems and provide insights into their design.
Bioinformatics Analysis of Toxicity and Functional Properties of Plant-Derived Bioactive Proteins Khatun, Most. Sharmin; Jahan, Afrin
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.112

Abstract

The main objective of this review is to discuss the biological activities of plants and their potential for therapeutic use, as well as to highlight the many kinds of bioactive proteins. Plant-derived bioactive proteins are essential because of their many functional qualities and health advantages in a variety of domains, including nutrition, medicine, and agriculture. Plant-derived bioactive proteins have attracted a lot of attention because of their potential as medicines and health advantages. To improve comprehension and application, this study uses bioinformatic tools to present a thorough analysis of the toxicity and functional properties of these proteins. We examine the variety of bioactive proteins originating from plants, emphasizing their functions in anti-inflammatory, anti-cancer, and antibacterial properties. We evaluate these proteins' structural characteristics, binding affinities, and processes of interaction with target molecules using sophisticated bioinformatics technologies. A particular focus is on assessing possible toxicity, using in silico predictive algorithms to detect side effects and guarantee safety in medicinal applications. We also go over how to anticipate the functional characteristics of novel bioactive proteins by integrating proteomic and genomic data. There are many tools such as BLAST, Clustal Omega, Inter Pro Scan for the analysis of bioinformatic data have been reviewed here. This study emphasizes how important bioinformatics is to understand the safety and therapeutic potential of bioactive proteins generated from plants, which opens the door to their optimal application in nutrition and medicine.

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