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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 11 Documents
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Search results for , issue "Vol 4, No 1 (2026)" : 11 Documents clear
Recent Advances in Thermal Management Techniques for High-Performance PMSMS: A Comprehensive Review Azom, Md Ali; Khan, Md. Yakub Ali
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.178

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.
Comparative Review of Electrical and Thermal Modeling Techniques for PMSMs in Next-Generation Electric Vehicles Ahmed, Abu Sayed Faisal; Uddin, Md Jasim; Hasan Mia, Md Mehedi; Saleh, Md Abu
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.189

Abstract

The objective of this paper is comparative reviews of PMSM electrical and thermal models for next-generation electric vehicles. The growing demand for electric vehicles (EVs) has necessitated advancements in motor technologies, with Permanent magnet synchronous motors (PMSMs) emerging as a dominant choice for the next-generation EV powertrains due to their high efficiency, compact design, and excellent torque characteristics. However, the performance and reliability of PMSMs in EVs are significantly affected by electrical and thermal behaviors, which are critical for optimizing their efficiency, longevity, and thermal management. This review provides a comprehensive comparison of various electrical and thermal models used to simulate and analyze PMSMs for EV applications. Electrical models focus on accurate representation of motor dynamics, including the influence of control techniques such as Field-Oriented Control (FOC) and Direct Torque Control (DTC). Conversely, the goal of thermal models is to forecast the motor's thermal performance by accounting for heat production, cooling techniques, and how temperature affects electrical and magnetic characteristics. Thermal modeling techniques remain relatively underdeveloped. Most models use simplified lumped parameter thermal networks (LPTNs) or basic steady-state approaches, which fail to capture spatial and temporal temperature gradients across components like windings, stator core, rotor, and bearings. The strengths and limitations of lumped-parameter models, finite element analysis (FEA), and coupled Multiphysics simulations in representing the intricate relationships between the electrical and thermal domains are compared in depth. The study also discusses new advancements, such as the application of machine learning methods for real-time monitoring and model optimization. Lastly, potential prospects for enhancing model fidelity and computing efficiency are outlined, as well as the difficulties in accurately predicting thermal behavior under dynamic operating settings.
[RETRACTED] Challenges and Breakthroughs in the Production of Composite Materials from Sustainable and Renewable Resources Kadir, Md Moin; Pranto, Jubaer Akon; Mia, Md Mehedi Hasan
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.122

Abstract

Following a rigorous, careful concerns and considered review of the article published in the Control Systems and Optimization Letters, to article entitled "Challenges and Breakthroughs in the Production of Composite Materials from Sustainable and Renewable Resources," Vol. 4, No. 1, pp. 1-7, 2026, https://doi.org/10.59247/csol.v4i1.122.This paper has been found to be in violation of the Control System and Optimization Letters Publication principles and has been retracted.The article contained redundant material; the editor investigated and found that the paper was published in Scientia. Technology, Science and Society, Vol. 2, No. 8, pp. 61-72, 2025, DOI: https://doi.org/10.59324/stss.2025.2(8).06, entitled "Challenges and Breakthroughs in the Production of Composite Materials from Sustainable and Renewable Resources".The document and its content have been removed from the Control Systems and Optimization Letters, and reasonable effort should be made to remove all references to this article.
Performance Analysis and Visual Evaluation of A Deep Learning-Based Wildfire Detection System Shifat, Sk Md Raihan; Joarder, Humayra Atia; Hasan, Md Najmul
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.239

Abstract

Wildfires represent a critical threat to ecosystems, human safety, and economic stability, emphasizing the necessity for rapid and reliable detection mechanisms. Traditional approaches such as satellite monitoring and manual surveillance are often hindered by latency, limited spatial resolution, and environmental constraints, thereby underscoring the value of automated and intelligent solutions. This study presents DeepFire, a real-time wildfire detection framework developed using the YOLOv8 architecture. Data preprocessing involved normalization, removal of irrelevant objects, and extensive data augmentation to enhance generalization and mitigate potential overfitting. The dataset encompassed diverse environmental conditions, including varying smoke intensities, vegetation densities, and viewing perspectives. Experimental evaluation demonstrated outstanding performance, achieving a mean Average Precision (mAP@0.5) of 0.995, precision and recall values of 0.995, and an F1-score of 1.00 at the optimal confidence threshold for detection. The mAP@0.5 metric was selected for its suitability in assessing localization accuracy under real-time constraints, whereas mAP@0.5:0.95 is discussed in the main text for comprehensive benchmarking. Qualitative assessments further verified the model’s robustness in accurately classifying a wide range of fire and non-fire scenarios. Future research will focus on enhancing dataset diversity, improving deployment efficiency, and validating system performance under real-world conditions.
Techno-Economic Assessment of Hybrid Renewable Micro Grids for Sustainable Rural Electrification Rabbany, Golam; Kumar, Swarup; Dhar, Uniwan-E-mi; Islam, Md Shoriful; Sarker, Md Tousiat
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.267

Abstract

This paper presents a stochastic and climate-informed techno-economic optimization framework for the optimal design of off-grid hybrid renewable energy micro grids aimed at sustainable rural electrification. The proposed system integrates solar photovoltaic (PV) generation, wind turbines, battery energy storage systems (BESS), and a diesel generator as backup to ensure reliable electricity supply under uncertain demand and variable renewable resources. Monte Carlo–based stochastic load modeling and climate-adjusted renewable resource assessment are employed to capture site-specific operating conditions. System sizing and operation are optimized using a multi-objective cost minimization approach targeting the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC), subject to predefined reliability and operational constraints, including a Loss of Power Supply Probability (LPSP ≤ 5%). Simulation results demonstrate that the optimized hybrid microgrid configuration reduces the LCOE by approximately 18–25% and diesel fuel consumption by over 40% compared to conventional deterministic designs, while achieving a renewable energy penetration exceeding 85%. In addition, the proposed framework leads to an estimated reduction in CO₂ emissions of about 45%, enhancing long-term environmental sustainability. These findings confirm that incorporating stochastic demand representation and climate-aware resource evaluation significantly improves the economic viability, reliability, and affordability of hybrid renewable microgrids for electrifying remote rural communities.
A Hybrid Quantum-Classical Optimization Model for Reconfigurable Intelligent Surfaces in 6G Networks Zangana, Hewa Majeed; Sulaiman, Maryam A.
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.276

Abstract

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for sixth-generation (6G) wireless networks by providing programmable control over the radio propagation environment. However, optimizing RIS configurations in large-scale and dynamic 6G scenarios remains a computationally intensive and non-convex problem, particularly under realistic channel conditions involving user mobility, multi-user interference, and fading effects. This paper proposes a hybrid quantum–classical optimization framework that integrates a Variational Quantum Eigensolver (VQE)–based optimization module with classical iterative solvers to efficiently configure RIS phase shifts and reflection coefficients. The quantum component facilitates probabilistic exploration of the high-dimensional and combinatorial search space associated with large RIS deployments, while the classical component enforces system constraints and ensures convergence stability. Simulation results under realistic 6G channel models demonstrate that the proposed hybrid approach achieves up to 32% faster convergence, 18–25% improvement in spectral efficiency, and notable energy efficiency gains compared to state-of-the-art classical optimization techniques. Furthermore, the framework exhibits scalable performance with increasing RIS element counts and user density, highlighting its suitability for near real-time RIS control under noisy intermediate-scale quantum (NISQ) hardware constraints. These findings indicate that hybrid quantum–classical optimization constitutes a practical and scalable solution for intelligent, adaptive, and energy-efficient RIS-assisted 6G networks.
Real-Time Trajectory Tracking Control of a DC Motor Using a Self-Tuning Regulator with Online Parameter Estimation Nguyen, Quang-Thien; Le, Hoang-Linh; Nguyen, Anh-Huy; Nguyen, Duc-Anh-Quan; Nguyen, Van-Dong-Hai; Nguyen, Minh-Tam; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Phuong-Quang; Le, Thi-Hong-Lam; Nguyen, Binh-Hau; Vu, Dinh-Minh
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.270

Abstract

An adaptive Self-Tuning Regulator (STR) is developed for DC motor control to address performance degradation caused by load disturbances and parameter uncertainties. The method combines online system identification using recursive least squares (RLS) with automatic controller retuning in discrete time. The motor dynamics are continuously estimated and used to update the controller parameters through a pole-placement (or minimum-variance) design, thereby maintaining the desired closed-loop response without manual gain adjustment. The STR is implemented in real time and tested under speed reference changes and varying load torque. Results confirm that the proposed approach enhances tracking performance and disturbance rejection compared with conventional fixed-gain control, making it suitable for practical DC drive systems operating under changing conditions.
Design and Implementation of an RFID-Based Smart Parking Control System with Infrared Sensors and Queueing-Theory Traffic Modelling Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.261

Abstract

The growing trends in the need for automated and trusted parking control have fostered the enhancement of smart systems that can do effective access control, precise occupancy identification, and effective traffic control. This paper provides both the design, implementation, and analytical evaluation of a low-cost smart parking control system based on Radio Frequency Identification (RFID)- based vehicle authentication, Infrared (IR) sensor-based slot monitoring, and servo-controlled gate actuation based on an Arduino-based embedded architecture. A working prototype is created to exemplify a one-level parking system, where IR sensors are used to detect live availability slots, an RFID module is used to provide authenticated access, and a Liquid Crystal Display (LCD) device is used to show occupants of the slot. In order to come up with a strict performance evaluation, Queueing Theory is adopted by modelling the entrance gate as a service system of M/M/1. This analytical model can be used to measure waiting times, queue time, and system usage quantitatively at different rates of arrival. Measurements conducted during experimental evaluation involve the accuracy of IR detection, RFID authentication latency, servo response time, system reliability, error-rate characterization, and analysis of energy consumption of each hardware component. These results indicate high accuracy in operation, fast authentication, consistent actuation operation, and low power consumption, applicable when a device is compact or battery powered. The queueing-based modelling also substantiates the fact that the system operates efficiently at the levels below saturation points.
Modeling of a Sliding Mode Controller to control the Tracking System for Solar Panels with Two Degrees of Freedom Ebrahim, Ali Aniss
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.277

Abstract

In this report, the design of a cascade Sliding Mode Controller (SMC) with a boundary layer for chattering attenuation is presented for a dual-axis solar tracking system. First, the theoretical background of this control method is presented. Then, using MATLAB/Simulink, the proposed controller is designed and simulated under various scenarios including step, sinusoidal trajectories, and external disturbances. The simulation results demonstrate high-precision tracking with a root mean square error (RMSE) of 0.15°, robust disturbance rejection, and a significant enhancement in electrical energy generation by up to 40% compared to a fixed-panel system.
Inner Loop-Based Robust Control Design Considering Uncertain Grid Impedance for a Single-Phase AC–DC Converter In, Sokvan; Choeung, Chivon; San, Sokna; Yay, Socheat; Siren, Seven; Srun, Channareth
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.293

Abstract

Single-phase AC–DC converters based on an H-bridge active rectifier topology are widely used in applications such as electric vehicle charging and renewable energy interfaces. To achieve zero steady-state error using integral control, it is essential to regulate the output in the dq-synchronous frame. A key challenge in controlling single-phase power converters is the inability to directly convert single-phase signals to dq-frame signals. This paper proposes the use of a digital all-pass filter to generate β-signals, which provide the orthogonal component required for dq-transformation in single-phase systems. The control strategy involves an outer loop proportional–integral (PI) controller for regulating the output DC voltage, while an inner loop a linear matrix inequality (LMI)-based robust state-feedback controller with integral action is employed to regulate the AC current. The dq-frame transformation enables effective current regulation, while the robust control law ensures closed-loop stability based on Lyapunov function in the presence of parameter uncertainties. The robustness of this control approach is demonstrated by considering system uncertainties, including variations in the filter inductance with nominal value 3 mH, and the effectiveness of the proposed control is confirmed through simulation results under different resistive load conditions, demonstrating stable operation and accurate DC voltage regulation. Future work will focus on experimental validation of the proposed control strategy and investigation of converter performance under grid disturbance conditions such as voltage unbalance and harmonic distortion.

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