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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 118 Documents
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AI-Driven Microgrid Solutions for Enhancing Energy Access and Reliability in Rural and Remote Areas: A Comprehensive Review Ahmed, Faisal; Uzzaman, Asif; Adam, Md Ibrahim; Islam, Monirul; Rahman, Md Moklesur; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.183

Abstract

As localized energy systems, microgrids provide a viable way to solve problems with energy dependability and access in rural and isolated locations. These regions often have inadequate and unstable grid infrastructure, which restricts their access to energy. Artificial Intelligence (AI) improves the overall performance, flexibility, and efficiency of microgrid systems. AI ensures a steady and dependable power supply by enabling predictive maintenance, optimal load forecasting, energy storage management, and renewable energy resource optimization. AI may help microgrids anticipate system faults, better control energy consumption, and prolong the life of vital parts. Additionally, AI ensures the sustainability of microgrids in resource-constrained places by optimizing the usage of renewable energy sources like solar and wind. Successful case studies from places like the US, India, and Africa have shown the promise of AI-enhanced microgrids in raising the standard of living for marginalized areas, despite obstacles like data infrastructure and upfront installation costs. Microgrids have a bright future thanks to developments in artificial intelligence (AI), which might increase electricity availability and promote economic growth in rural and isolated regions of the world.
Advancements in AI-driven Cotton Fiber Quality Assessment Through Image Processing: A Comprehensive Review Prudente, Marc Joshua; Arboleda, Edwin R.; Gutierrez, Joshua Balistoy
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.v3i2.164

Abstract

The integration of artificial intelligence (AI) and image processing techniques has emerged as a transformative solution to address the limitations of traditional cotton fiber quality assessment methods, particularly the High-Volume Instrument (HVI) and Advanced Fiber Information System (AFIS), which require time-consuming manual labor. This comprehensive review examines the convergence of three key technological domains: image processing, AI/machine learning, and IoT/edge computing, in revolutionizing cotton fiber quality assessment. The review focuses on three primary image processing techniques—feature extraction, segmentation, and classification—that enable precise analysis of critical fiber properties including length, fineness, strength, and maturity. Advanced AI algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in automating the assessment process, achieving accuracy rates of 82-98% in fiber classification tasks. The integration of Internet of Things (IoT) devices and edge computing has further enhanced the system's capabilities, enabled real-time quality assessments and reduced processing time by up to 60% compared to traditional methods. However, several significant challenges persist, including limited availability of high-quality annotated datasets, variability in image quality due to environmental factors, model generalization across different cotton varieties, and real-time processing constraints in industrial settings. The combination of image data with additional sensor inputs, such as spectral analysis and environmental monitoring, offers potential to further enhance assessment accuracy and robustness. This review emphasizes the transformative potential of AI-driven image processing systems in revolutionizing cotton fiber quality assessment, while also identifying critical areas requiring further research for successful industrial implementation. The findings suggest that continued advancements in AI algorithms, coupled with improved IoT integration and edge computing capabilities, will be crucial for developing more robust and efficient quality assessment systems in the cotton industry.
Quantum-Behaved Particle Swarm Optimization-Tuned PI Controller of a SEPIC Converter Perkasa, Sigit Dani; Megantoro, Prisma; Jasmine, Senit Araminta
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.v3i2.186

Abstract

The Single-Ended Primary Inductor Converter (SEPIC) is vital for voltage regulation in dynamic systems like renewable energy and electric vehicles. Traditional PI controllers struggle with tuning complexity and oscillations. This study introduces Quantum-Behaved Particle Swarm Optimization (QPSO) to optimize PI gains (Kp, Ki) for SEPIC converters. QPSO improves global search by using quantum-inspired probabilistic motion, overcoming issues of premature convergence seen in traditional PSO. Four objective functions—ISE, ITAE, IAE, and MSE—were evaluated to balance transient and steady-state performance. ITAE and IAE outperformed others, minimizing overshoot to 1.26% in boost mode and achieving the fastest settling time of 1,872 s. Sensitivity analysis revealed that Ki 2.0 destabilizes the system, while Kp 1.5 increases voltage ripples. The framework is computationally efficient, ideal for embedded applications. Future work should include hardware-in-loop testing to confirm robustness.
Review of Design Innovations and Efficiency in Hydraulic Valve Control Systems Kumar, Sree Biddut; Acharjee, Partho Protim; Hossen, Md Saim; Basak, Pallab
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.v3i2.187

Abstract

This paper explores the evolution and innovations in hydraulic valve control systems, emphasizing their role, technological advancements, and current challenges. Hydraulic valves are essential components in regulating fluid flow, pressure, and direction within hydraulic systems, and are widely used in industries such as aerospace, automotive, manufacturing, and mobile machinery. Recent innovations in valve materials, miniaturization, and intelligent control systems have significantly enhanced the design and operational efficiency of hydraulic systems. The integration of electro-hydraulic, proportional, and servo control technologies has improved precision and adaptability in managing fluid dynamics. These developments have led to notable gains in energy efficiency, system reliability, and performance. The introduction of digital control systems and machine learning has further expanded possibilities, enabling real-time monitoring, predictive maintenance, and remote-               control capabilities. Despite these advancements, challenges remain particularly in addressing high material costs, integration complexities, and the ongoing need for more energy-efficient solutions.
A Comprehensive Review of Harnessing Bioinformatics in Biochemistry: A New Era of Data-Driven Discoveries and Applications Kimu, Amina Khatun
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.168

Abstract

The integration of bioinformatics into biochemistry has ushered in a new era of scientific discovery, leveraging computational power and big data to uncover molecular mechanisms, predict molecular interactions, and accelerate the development of therapeutics. This review explores the advancements in bioinformatics tools and techniques that are transforming biochemistry. By discussing key applications, such as protein structure prediction, genomic data analysis, and systems biology, this paper highlights the significant contributions of bioinformatics in biochemistry and its potential for future applications in personalized medicine, drug discovery, and disease modeling. A key factor in the advancement of biochemistry, bioinformatics has become a transformative field at the nexus of biology, computer science, and statistics. Using tools and methods from genomics, proteomics, drug discovery, and systems biology, this review examines how bioinformatics might be integrated into the study of biochemical processes. The study of multi-omics data, the use of machine learning techniques to find molecular patterns and biological insights, and the application of computational modeling for protein structure prediction are important subjects. The paper also looks at the difficulties in analyzing biological data on a big scale, such as problems with data quality, reproducibility, and the requirement for interdisciplinary cooperation. As new technologies like artificial intelligence and quantum computing become available, bioinformatics has the potential to completely transform our knowledge of biological systems and speed up the identification of new biomarkers and treatment targets. This era of data-driven science promises to enhance human health through advancements in personalized medicine and innovative solutions to complex biochemical challenges.
Antioxidant Peptides from Proteins: Separation, Identification, Mechanisms, and Applications in Food Systems Khatun, Most. Sharmin
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.v3i2.192

Abstract

With an emphasis on their identification, separation, modes of action, and possible uses in food systems, this work offers a thorough investigation of antioxidant peptides generated from bioactive proteins. Antioxidant peptides are essential for combatting free radicals and averting oxidative damage. They are often generated by the enzymatic degradation of proteins from diverse sources, including plants, animal, and marine sources. High-performance liquid chromatography (HPLC), ultrafiltration, membrane filtration, and mass spectrometry are some of the sophisticated techniques used in the separation and identification of these peptides. These techniques enable accurate isolation and characterization. It's important to comprehend the processes by which these peptides exercise their antioxidant benefits; research suggests that their main modes of action include suppression of lipid peroxidation, metal ion chelation, and free radical scavenging. These bioactive peptides have a great deal of promise to improve food items' functional and nutritional qualities. Their integration into food systems can enhance their nutritional content, self-life, and health advantages, making them important components in the creation of functional meals. While the study highlights the promising potential of bioactive peptides, further research is essential to evaluate their stability, bioavailability, and safety under real-world conditions. Factors such as gastrointestinal degradation, absorption efficiency, and potential toxicity must be thoroughly assessed to ensure practical applicability. In addition, this work addresses the difficulties pertaining to these peptides' stability and bioavailability in food matrices and identifies areas for future investigation to maximize their application in the food sector.
Testing Autonomous Vehicles in Virtual Environments: A Review of Simulation Tools and Techniques Uzzaman, Asif; Islam, Monirul; Hossain, Md Shimul
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.v3i2.196

Abstract

Autonomous vehicles (AVs) have the potential to transform the transportation industry by improving road safety, reducing traffic congestion, and enhancing fuel efficiency. Significant progress has been made in autonomous vehicle (AV) technologies, especially in sensor systems, machine learning, and artificial intelligence. These advancements enable vehicles to navigate complex environments and make real-time decisions. Despite these advancements, numerous challenges remain in ensuring the safety, reliability, and acceptance of AVs. Key issues include sensor fusion, the ability to handle unpredictable scenarios, the development of universally accepted regulatory frameworks, and public trust in autonomous systems. Furthermore, ethical dilemmas, such as decision-making in unavoidable accident situations, present additional concerns. The deployment of AVs also raises questions about the impact on employment in driving-dependent industries and the infrastructure needed to support these technologies. This paper reviews the current state of AV development, examining the progress made in simulation-based testing, sensor technology, and decision-making algorithms. Additionally, it discusses the challenges that still need to be addressed, including safety concerns, regulatory barriers, and societal implications. The paper concludes by outlining potential areas for future research, such as improving sensor reliability, enhancing machine learning algorithms, integrates an analysis of simulation-based testing, decision-making algorithms, and sensor technologies with a forward-looking discussion on legal frameworks, public trust, and employment impacts, offering a holistic perspective on the path toward AV integration.
The Aries Metaheuristic Algorithm: Exploring Global Optimization Through Impulse, Passion, and Adventure 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.v3i2.210

Abstract

As optimization algorithms are increasingly used in various fields, metaheuristic algorithms have become a research hotspot due to their powerful global optimization capabilities. Inspired by Aries's adventurous spirit, passion, and motivation, this paper proposes a new metaheuristic algorithm, the Aries metaheuristic algorithm (AMA), which aims to optimize the objective function in multidimensional complex problems. This paper elaborates on the design concept, algorithm flow, and characteristics of AMA, and demonstrates the advantages of AMA in global search through experimental verification on classic benchmark functions and practical problems. Finally, compared with traditional algorithms such as particle swarm optimization (PSO), differential evolution (DE), simulated annealing (SA), and random search (Random), AMA has been shown to have superior performance in solving optimization problems. The core innovation of AMA lies in its impulsive search, emotion-driven jumping, and collective cooperation mechanisms, which simulate Aries-like psychological dynamics to guide the global optimization process.
Effects of Static and Dynamical Disturbance Forces on the Performance of a Wire Driven Flexible Robot Tahmasebi, Mona; Gohari, Mohammad; Zolfagharian, Ali; Zare, Mohammad Reza; Pak, Abbas
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.v3i2.34

Abstract

Robot requests are increased by recent development of IoT, telemetry and human requirements in uninteresting or precision jobs such as surgery, industrial inspections or crops harvesting. Numerous robots are industrialized by researchers for various tasks. Flexible robots are developed based on declared requests since they can adapt their geometry to the working circumstances. Existing study presents a wire driven flexible robot enthused of animal organs such as octopus tentacles or elephant`s trunk. It can move in planar and space based on assembly of that. Primarily, a kinematic model founded to estimate end effector location, formerly a dynamic model established to compute essential tension of tendon based on bending beam theory. Moreover, effects of static and dynamical load applied on the WDFR are studied as external disturbances. A test rig is fabricated to assess attained models. The results demonstrate close convergence between tests outcomes and outputs of models. Accordingly, dynamic and kinematic models can be operated in design of controller in coming works.
The Intersection of Remote Sensing and Biomedical Imaging: A Review of Techniques for Cancer Detection Islam, Asm Mohaimenul; Drishty, Shohely Muntaha; Alam, Md. Adnanul
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.v3i2.207

Abstract

Early and accurate cancer detection remains a major challenge in modern healthcare. This study explores the emerging intersection between biomedical imaging and remote sensing technologies, focusing on the adaptation of microwave radar, thermal imaging, multispectral imaging, and hyperspectral imaging (HSI) for non-invasive cancer diagnosis. Originally developed for environmental and aerospace applications, these technologies are now being repurposed to detect subtle biochemical, morphological, and metabolic changes in human tissues that signal the presence of cancer. When integrated with artificial intelligence and machine learning, these imaging modalities enable real-time classification, high-throughput analysis, and enhanced surgical guidance. We highlight their advantages over traditional imaging methods and examine their application in detecting malignancies of the brain, breast, skin, oral cavity, and gastrointestinal tract. This review also discusses critical clinical and technical challenges, including the lack of standardized datasets, issues with device mobility, data complexity, and regulatory hurdles. Finally, we outline promising future directions such as edge-based data processing, explainable AI systems, multimodal imaging fusion, and ethical considerations for deployment in resource-constrained settings. This multidisciplinary approach has the potential to revolutionize cancer diagnostics, making them faster, safer, and more accessible, particularly for underserved populations.

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