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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 124 Documents
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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.

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