cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Alfian Ma'arif
Contact Email
alfian_maarif@ieee.org
Phone
-
Journal Mail Official
alfian_maarif@ieee.org
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
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
 1   2 
Search results for , issue "Vol 3, No 2 (2025)" : 15 Documents clear
Classical Dance-Metaheuristic: A Metaheuristic Optimization Algorithm Inspired by Classical Dance Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.206

Abstract

This paper proposes a metaheuristic optimization algorithm based on classical dance, namely the Classical Dance Metaheuristic (CDMH). The algorithm combines the core elements of ballet, Indian classical dance and Chinese classical dance with modern optimization techniques, providing a new approach to high-dimensional optimization problems. The CDMH algorithm optimizes the search process through three stages of simulation: the posture training stage in bllaet, the rhythm and mudra exploration stage in Indian classical dance, and the integration stage of body, rhythm and artistic conception in Chinese classical dance. Experimental results show that CDMH shows good optimization ability in multiple classic optimization problems and can effectively avoid the dilemma of local optimal solutions.
Energy Management Strategies for Electric Vehicle Charging in Microgrids: A Case Study of Optimization Techniques Akash, Khairul Bashar; Akter, Mst Sumi; Emon, Md Afrad Hasan; Kazmi, Muhammad Meisam; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.202

Abstract

The integration of Electric Vehicles (EVs) into microgrids presents both significant opportunities and complex challenges in energy management. As the adoption of EVs increases, efficient charging strategies become essential for maintaining grid stability, reducing energy costs, and maximizing the utilization of renewable energy sources. This review explores various optimization techniques applied to energy management in EV charging within microgrids, including deterministic approaches, stochastic programming, Model Predictive Control (MPC), game theory, machine learning, and heuristic/metaheuristic methods. Each technique is evaluated based on its strengths, weaknesses, and applicability to different system requirements, such as real-time responsiveness, adaptability to uncertainties, and scalability. Moreover, the paper identifies emerging trends and key research areas, such as hybrid optimization frameworks, decentralized energy markets, Vehicle-to-Grid (V2G) technology, and the integration of explainable AI for enhanced decision-making transparency. Additionally, challenges related to cybersecurity, resilience to system faults, and the integration of large-scale EV infrastructure are discussed. The paper concludes by highlighting the need for multi-objective optimization approaches that balance cost efficiency, user satisfaction, and grid reliability. With rapid advancements in EV technology and microgrid systems, research must focus on developing scalable and secure energy management solutions. While AI-driven methods show strong potential, real-world adoption faces challenges such as high costs, technical complexity, and integration issues. Practical applications highlight feasibility, but broader implementation demands further refinement.
Comparative Analysis of IoT and AI-Based Control Strategies for Community Micro-Grids Islam, Md Monirul; Akter, Mst. Tamanna; Elme, Nafisa Sultana; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.191

Abstract

The main objective of this paper is to review the centralized, decentralized, and hybrid control approaches based on key performance metrics such as efficiency, reliability, and scalability. By improving sustainability, dependability, and efficiency, the combination of artificial intelligence (AI) and the Internet of Things (IoT) in community micro-grids has completely changed energy management. The Internet of Things (IoT) and artificial intelligence (AI) have been used more and more in microgrid control to improve autonomy, dependability, and efficiency. Sensors, smart meters, distributed energy resources (DERs), and energy storage systems are just a few of the microgrid's components that can communicate and monitor in real time thanks to the Internet of Things.AI uses this data to make smart decisions on activities like fault detection, load forecasting, renewable energy prediction, and optimal power dispatch.  To optimize power distribution, load balancing, and fault detection in micro-grids, this article compares several control systems that make use of IoT and AI. The study looks at decentralized, hybrid, and centralized control strategies, emphasizing their benefits, drawbacks, and applicability in various operational scenarios. Important performance indicators are assessed, including cost-effectiveness, responsiveness, energy efficiency, and flexibility about renewable energy sources. The results contribute to the development of smart energy systems by shedding light on the best control schemes for enhancing microgrid performance.
A Comprehensive Review of AI-Driven DC Arc Fault Detection in Photovoltaic Systems Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.208

Abstract

Photovoltaic (PV) systems now account for over 1.3% of global electricity generation and are expanding rapidly. DC arc faults pose severe safety risks among PV system faults, including fire hazards, equipment damage, and system failures. Traditional protection methods such as overcurrent devices and threshold-based detection often fail to distinguish arc faults from normal system noise reliably due to series arcs' intermittent and low-current nature. To address these problems, intelligent methods, including machine learning (ML), deep learning (DL), and hybrid approaches, have emerged as promising solutions offering superior accuracy, adaptability, and real-time performance. This paper presents a state-of-the-art intelligent approach for DC arc fault detection in PV systems. We explicitly compare ML and DL algorithms, highlighting trade-offs in computational efficiency, data requirements, and hardware constraints. Key implementation challenges, limited real-world datasets, and high computational costs for edge deployment are analyzed. Future directions focus on bridging gaps such as edge computing for real-time detection, synthetic data generation, and interpretable AI models. The findings aim to advance PV safety standards and enable scalable renewable energy integration.
Duck Foraging Algorithm (DFA): A Metaheuristic Algorithm Inspired by Duck Foraging Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.215

Abstract

For many complex optimization problems, the solution process often involves exploring multidimensional space, balancing global and local solutions, and improving the efficiency of the algorithm. In order to improve the optimization efficiency, this paper proposes a new metaheuristic algorithm called the Duck Foraging Algorithm (DFA). The algorithm is inspired by the behavior patterns of wild ducks in nature when foraging, especially their intra-group cooperation, clear division of labor, territoriality, and mobile foraging strategies. By simulating the foraging behavior of ducks, DFA can effectively explore and develop complex solution spaces and find the global optimal solution. The core principles and processes of the algorithm are elaborated in detail and compared with existing optimization algorithms. Finally, we verify its superiority in different types of optimization problems through a series of numerical experiments. Compared with traditional algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC), DFA incorporates unique behavioral mechanisms—such as dynamic leadership switching and decentralized area foraging—based on duck group strategies. In particular, the leader duck guides the group based on fitness ranking, while other ducks balance local search and migration, reflecting a cooperative yet diversified exploration strategy.

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 4, No 1 (2026) Vol 3, No 3 (2025) Vol 3, No 2 (2025) Vol 3, No 1 (2025) Vol 2, No 3 (2024) Vol 2, No 2 (2024) Vol 2, No 1 (2024) Vol 1, No 3 (2023) Vol 1, No 2 (2023) Vol 1, No 1 (2023)