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 18 Documents
 1   2 
Search results for , issue "Vol 3, No 3 (2025)" : 18 Documents clear
DC Arc Fault Detection in Microgrids: A Comprehensive Review of Challenges, Advances, and Future Directions Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.244

Abstract

DC arc faults in residential, commercial, and industrial DC microgrids pose significant safety and reliability challenges, including potential fire hazards, equipment damage, and system downtime. Despite advancements in detection technologies, accurately detecting and mitigating DC arc faults remains difficult due to the dynamic nature of microgrids, fluctuating load conditions, and the absence of zero-crossing points in DC systems. This review provides a thorough analysis of existing DC arc-fault detection methods, including time-domain, frequency-domain, time-frequency analysis, and machine learning techniques, and compares their performance in terms of accuracy, robustness, and real-time applicability. The review highlights the principles, advantages, and limitations of each approach, addressing key challenges such as noise interference, low-current arc detection, and the need for real-time processing. Furthermore, it discusses recent developments in hybrid detection systems, high-frequency signal processing, and deep learning models as promising solutions to enhance detection accuracy and system reliability, while also addressing practical implementation challenges. Finally, the review outlines future research directions, emphasizing the importance of adaptive algorithms, standardized testing protocols, and integration with emerging grid technologies. This review distinguishes itself by providing a systematic comparison of detection paradigms and a synthesized roadmap for future research, bridging the gap between theoretical advances and practical implementation in diverse microgrid environments.
Recent Advances in Thermal Management Techniques for High-Performance PMSMs - A Comprehensive review Azoma, Md Ali; Khanb, Md Yakub Ali
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.180

Abstract

This paper presents recent advancements in thermal management techniques for high-performance permanent magnet synchronous motors (PMSMs), highlighting their role in improving efficiency and reliability. Due to their high-power density, small size, and exceptional efficiency, permanent magnet synchronous motors, or PMSMs, have become indispensable in high-performance applications like industrial automation, electric cars, and aerospace. Since excessive heat generation can lower motor efficiency, shorten its lifespan, and jeopardize dependability, the growing demands for improved performance have created serious issues in thermal management. The latest developments in thermal management strategies for high-performance PMSMs are thoroughly examined in this paper. Important thermal issues are covered, such as the development of hot spots, unequal heat distribution, and thermal resistance at crucial contacts. The incorporation of cutting-edge cooling technologies into motor design is examined in this research, along with liquid cooling, heat pipes, phase change materials, and enhanced thermal interface materials. Furthermore, it is emphasized how important computational thermal modeling and simulation are to maximizing PMSM thermal performance. The interaction of mechanical, thermal, and electrical dynamics is emphasized to provide dependable and effective motor operation. This review identifies current limitations and explores future trends, including adaptive cooling techniques and AI-driven thermal modeling, to enhance PMSM efficiency and sustainability. This will allow for further breakthroughs in sustainable and energy-efficient technologies.
Human Breakfast Selection Algorithm (HBSA): A Human-Inspired Metaheuristic for Constrained Optimization Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.214

Abstract

In this paper, we propose a new metaheuristic algorithm inspired by human daily breakfast choice behavior, namely the human breakfast choice algorithm (HBSA). When deciding what to eat for breakfast, people often consider multiple goals, constraints, and personal preferences. The algorithm simulates the memory mechanism, preference guidance, contextual adaptation, and hybrid decision-making strategies of human breakfast choices to achieve more effective exploration capabilities in solving combinatorial optimization problems. We apply the algorithm to a typical 0-1 knapsack problem and conduct comparative experiments with genetic algorithms (GA) and particle swarm optimization algorithms (PSO). The results show that the improved HBSA performs better in terms of solution quality and stability.
Role of Business Analytics and Geospatial Data in Evaluating Climate Adaptation and Energy Transition Policies in the United States: A Case Study Shahiduzzaman, Md; Hasan, Md Maksudul
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.242

Abstract

This paper investigates how integrating business analytics and geospatial data can enhance the assessment of climate adaptation and energy transition policies in the United States. The study aims to develop and test an analytical framework that quantitatively evaluates policy effectiveness, resilience, and equity across spatial and temporal scales. Using predictive modeling, spatial clustering, and multi-criteria optimization, the framework combines policy, climate, and energy datasets to identify trends, vulnerabilities, and opportunities for sustainable transformation. Three U.S. case studies urban heat adaptation in Phoenix, renewable energy deployment in Texas, and disaster resilience planning in coastal Louisiana demonstrate the framework’s application. The analysis reveals that regions leveraging data-driven strategies achieve up to 18% higher efficiency in renewable integration and greater adaptive capacity in extreme heat management. These findings highlight the framework’s ability to translate complex geospatial and analytical insights into actionable policy guidance. By uniquely integrating business analytics with geospatial intelligence, this research offers a novel, evidence-based approach to evaluating climate and energy transition policies, contributing to both methodological innovation and practical policymaking for a low-carbon, climate-resilient future.
A Comprehensive Review of AI-Driven Forecasting and Energy Management for DC Microgrids with High Renewable Integration Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.245

Abstract

The global transition toward decarbonization has led to a greater integration of renewable energy sources (RES) into power systems, facilitating the widespread adoption of direct current (DC) microgrids. DC microgrids are particularly compatible with modern power systems because they support solar photovoltaic systems, batteries, and electronic loads. Despite these advantages, high levels of intermittent RES introduce challenges related to power balance, voltage stability, and reliable operation. Artificial Intelligence (AI) has emerged as a critical tool, enabling advanced forecasting and intelligent energy management systems (EMS) to address these issues. This comprehensive review examines state-of-the-art AI-based methods for DC microgrids, analyzing a wide range of studies from simulation-based models to real-world experimental pilots. It starts with an overview of the system architecture and operational challenges, followed by a novel taxonomy of AI approaches. The review critically compares machine learning for forecasting and reinforcement learning for real-time control, highlighting their respective performance in handling uncertainty. AI-driven EMS strategies, especially reinforcement learning for optimal scheduling, are detailed. The symbiotic relationship between accurate forecasting and robust EMS is explored, along with challenges such as data dependency and model explain ability, for which emerging solutions, such as federated learning and explainable AI (XAI), are discussed. The paper concludes by outlining future research directions, such as federated learning and standardized benchmarks. It underscores this review's key contribution by providing an integrated framework that bridges the gap between AI-driven forecasting and control for resilient and efficient DC microgrid operation.
Advancements in Electric Vehicle Technologies: A Review of Powertrain Architectures and Battery Innovations Hasan Mia, Md Mehedi; Uddin, Md Jasim; Ahmed, Abu Sayed Faisal
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.190

Abstract

The main goal of this paper is to review the advancements in electric vehicle technologies for powertrain and battery innovations. The rapid evolution of electric vehicles (EVs) is driven by advancements in powertrain architecture and battery technologies, enabling improved efficiency, performance, and sustainability. This review explores recent innovations in EV powertrains, including permanent magnet synchronous motors (PMSMs), induction motors, and emerging switched reluctance motors (SRMs), along with advancements in motor control strategies and power electronics. Advanced control techniques like field-oriented control (FOC) and predictive control must be explained in detail for PMSM-based electric vehicle systems. FOC is a popular vector control method that converts the torque and flux components of the stator current into a rotating reference frame to decouple them. However, a more contemporary method called model predictive control (MPC) forecasts future system states using a dynamic model to optimize motor control operations. Additionally, battery technology developments, such as high-energy-density lithium-ion batteries, solid-state batteries, and next-generation fast-charging solutions, are analyzed in terms of energy storage capacity, charging speed, thermal management, and lifecycle improvements. The need for innovation is still shaped by practical obstacles in addition to technical developments in electric vehicle (EV) powertrains. Manufacturers are compelled to investigate alternative chemistries or optimize energy usage through more effective motor control and thermal management, for example, because lithium-ion batteries' reliance on rare and geopolitically sensitive materials like cobalt raises concerns about sustainability and the supply chain. The integration of powertrain and battery innovations with intelligent energy management systems and vehicle-to-grid (V2G) technology is also discussed, highlighting their impact on EV range, reliability, and grid sustainability. This review provides a comprehensive understanding of the current technological landscape and future directions in EV development, addressing key challenges such as material limitations, charging infrastructure, and cost-effectiveness.
Derivation of Necessary Conditions for Optimal Control of the Implicit Burgers’ Equation Using the Variational Principle Hashimoto, Tomoaki
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.246

Abstract

Controlling fluid dynamics is a challenging problem that arises in many fields including aeronautical, biological and chemical engineering. Burgers’ equation is used to describe fundamental flow phenomena. Implicit systems belong to a more generalized class of systems than a class of explicit systems, because they can additionally contain algebraic constraints. Although the optimal control problem of the explicit form of Burgers' equation has been already explored in many papers, the optimal control problem of implicit form of Burgers’ equation is still an open problem as far as general classes of systems are concerned. In this paper, necessary conditions for an evaluation function to be optimized are derived on the basis of the variational principle for the optimal control problem of the implicit form of Burgers’ equation.
AI-Driven Threat Intelligence on Blockchain Using Deep Learning for Decentralized Cyber Risk Prediction Zangana, Hewa Majeed; Beitollahi, Hakem; Muhamad, Sabat Salih; Mohammed, Aquil Mirza; Wani, Sharyar
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.262

Abstract

The increasing complexity of cyber threats such as advanced persistent threats (APTs), ransomware, distributed denial-of-service (DDoS), and smart contract exploits requires cybersecurity solutions that go beyond traditional centralized defenses. This paper proposes an AI-driven threat intelligence framework integrated with blockchain technology for decentralized and trustworthy cyber risk prediction. The novelty of the proposed framework lies in its hybrid architecture, where deep learning–based anomaly detection models (including LSTM and autoencoder networks) analyze real-time cybersecurity data—such as blockchain transaction logs, network activity records, and external threat intelligence feeds—while blockchain is used to securely store, validate, and share AI-generated threat intelligence in a tamper-resistant and decentralized manner. Unlike AI-only solutions that suffer from data integrity and trust issues, or blockchain-only approaches that lack intelligent threat detection, the proposed framework combines the strengths of both technologies to enhance detection accuracy and stakeholder trust. Experimental evaluation conducted in a simulated blockchain environment demonstrates a detection accuracy of 96.4%, a false positive rate of 3.6%, and effective identification of multiple attack categories, including smart contract exploits and 51% attacks. While the framework improves security and transparency for inter-organizational security teams, enterprise networks, and supply-chain partners, it also introduces challenges related to computational overhead and blockchain scalability. Overall, the results indicate that integrating AI-driven threat intelligence with blockchain offers a practical and robust solution for decentralized cybersecurity risk prediction.

Page 2 of 2 | Total Record : 18

 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)