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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 3, No 1 (2025)" : 15 Documents clear
Recent Developments in Control and Simulation of Permanent Magnet Synchronous Motor Systems Azom, Md Ali; Khan, Md. Yakub Ali
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.v3i1.173

Abstract

This paper's main goal is to present a thorough analysis of current advancements in the simulation and control of Permanent Magnet Synchronous Motor (PMSM) systems. A crucial part of contemporary electrical drive systems, the Permanent Magnet Synchronous Motor (PMSM) finds extensive use in fields like industrial automation, renewable energy systems, and electric cars. This review examines the most current developments in PMSM system control and simulation, with a focus on cutting-edge modelling techniques, new control strategies, and the most recent simulation methods. It emphasizes how increasingly complex strategies like Model Predictive Control (MPC), Sliding Mode Control (SMC), and AI-based approaches have replaced more conventional ones like PID and vector control. Advanced control techniques like Field-Oriented Control (FOC) and MPC are used by Tesla and other EV manufacturers to maximize PMSM performance, guarantee smooth torque delivery, and improve energy economy. Siemens Gamesa wind turbines use PMSMs with reliable control systems for fault tolerance and maximum energy production in a range of wind conditions. The study also discusses the developments in simulation techniques, such as the incorporation of multi-physics models, real-time simulation, and the application of AI to improve simulation efficiency and accuracy. More realistic modelling of PMSM systems in dynamic contexts is now possible thanks to recent developments in simulation approaches, such as Multiphysics models and real-time simulations. These simulations are combined with sophisticated control algorithms to give real-time input while the system is operating, which speeds up fault finding and optimization. This procedure is further improved by AI-based simulation tools, which forecast system behavior’s under varied circumstances and spot possible problems before they arise. It is described how these advancements affect PMSM performance, including increased fault tolerance, robustness, and efficiency. The study concludes by highlighting the significance of integrating cutting-edge control and simulation approaches for optimal performance in PMSM systems, as well as important research issues and prospects.
Optimized Photoplethysmography-Based Classification of Calf Muscle Fatigue Using Particle Swarm Optimization with Logistic Regression Perkasa, Sigit Dani; Ama, Fadli; Megantoro, Prisma
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.v3i1.182

Abstract

This study investigates photoplethysmography (PPG) as a non-invasive, cost-effective alternative for real-time muscle fatigue monitoring, addressing limitations inherent to conventional methods like electromyography (EMG) and blood lactate testing. A PPG-based system was developed to classify fatigued versus non-fatigued states of the calf muscle using a DFRobot SEN0203 sensor at a 1000 Hz sampling rate. The raw PPG signals were segmented into 1-second intervals and processed to compute first and second derivatives—yielding vascular (VPG) and arterial (APG) photoplethysmograms—which enabled extraction of key features including heart rate (HR), heart rate variability (HRV), peak systolic and diastolic voltages, maximum systolic slope (u), minimum diastolic slope (v), and arterial stiffness indicators (b–a and c–a ratios). A Particle Swarm Optimization (PSO) algorithm was employed to optimize both feature selection and hyperparameters within a Logistic Regression (LR) model, achieving perfect classification accuracy (1.0) with training and prediction times of 0.0053 s and 0.0016 s, respectively. Notably, HRV and the minimum diastolic slope—reflecting autonomic regulation and vascular compliance—emerged as the most influential features with weights of 12.3747 and 23.9367. Comparative analyses revealed that although LightGBM matched the PSO-LR accuracy, neural network approaches performed poorly (0.50 accuracy), likely due to overfitting and limited training data. These findings underscore the viability of PPG for muscle fatigue monitoring, with promising applications in sports science, rehabilitation, and occupational health.
Review on the Safety and Sustainability of Autonomous Vehicles: Challenges and Future Directions Uzzaman, Asif; Adam, Md Ibrahim; Alam, Shahin; Basak, Pallab
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.v3i1.185

Abstract

Autonomous vehicles (AVs) represent a major advancement in transportation technology, offering significant benefits such as enhanced road safety, reduced traffic congestion, and improved mobility. However, the widespread deployment of AVs faces key obstacles, including sensor limitations under adverse weather conditions, ethical decision-making in complex scenarios, regulatory challenges, and data privacy concerns. This paper examines these challenges and proposes potential solutions. Key challenges include improving sensor fusion and AI algorithms to enhance perception and decision-making, developing standardized ethical guidelines for autonomous systems, and establishing consistent legal and regulatory standards across regions. Additionally, ensuring cybersecurity and addressing data privacy issues are critical for maintaining the safety and trust of AV users. The future of AVs also depends on advancements in infrastructure, such as the development of smart roads and Vehicle-to-Everything (V2X) communication systems, as well as reducing production costs to increase accessibility. Furthermore, raising public awareness and fostering acceptance through education and transparent communication about AV benefits is vital. The paper concludes that with ongoing research, innovation, and collaboration, AVs have the potential to revolutionize transportation, offering a safer, more efficient, and sustainable future for mobility.
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.
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.

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