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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 154 Documents
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Design and Optimization of Structural Parameters of Hydraulic Retarder Blades Md Shimul Hossain; Wang Jiaxin; Md Ruqul Merda
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.v4i1.291

Abstract

Hydraulic retarders are auxiliary braking devices in heavy-duty vehicles where rotor blade structural integrity directly affects system reliability and safety. Under operational conditions, blades experience combined centrifugal and fluid pressure loading, making geometric optimization essential to prevent stress concentration and deformation failure. This study employs finite element analysis to conduct a systematic parametric investigation of rotor blade design. Four key parameters—blade number (32-36), thickness (3-5 mm), wedge angle (35°-50°), and material (structural steel, AISI 4140, aluminum bronze, CFRP)—were evaluated under identical operating conditions (2000 rpm rotational velocity, 0.5 MPa uniform pressure). Equivalent stress, deformation, strain, and safety factor were used as comparative metrics. Results demonstrate that geometric optimization significantly outperforms material addition in improving structural performance. The optimized configuration achieves substantially enhanced safety margins while maintaining deformation within elastic limits. Material comparison identifies AISI 4140 as offering the optimal balance of strength and stiffness. These findings provide quantifiable design guidance for hydraulic retarder development and establish a systematic optimization framework applicable to rotating machinery components.
Wheel Velocity Control for Electric Car with Kalman Filter and PID Optimization Falaah, Fadjar Nur; Ma'arif, Alfian
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.v3i3.176

Abstract

This research addresses the challenge of achieving precise rotational speed control for DC motors in electric vehicles, a critical factor for ensuring smooth operation, energy efficiency, and safety. The study integrates a Kalman Filter with a PID Controller to mitigate sensor noise and external disturbances while minimizing steady-state errors. The Kalman Filter effectively reduces noise from rotary encoder sensors, enabling accurate speed estimation with multiplier values of countPulseM1 = 20.0 and countPulseM2 = 40.9. Optimal Kalman Filter parameter ratios were identified as R = 10.0, Q = 0.0001 for motor M1 and R = 8.0, Q = 0.0001 for motor M2, which minimized noise but resulted in slower motor responses compared to lower ratio configurations. To address this limitation, the PID controller was fine-tuned, yielding optimal parameters of Kp = 1.1, Ki = 8.1, and Kd = 0.00036 for motor M1 and Kp = 0.9, Ki = 9.4, and Kd = 0.00009 for motor M2. These settings achieved a rise time of 0.13 seconds, overshoot of 8.69%, and steady-state error of -1.19%. Disturbance testing with a hall magnetic rotary encoder revealed motor M1 with a rise time of 0.27 seconds and M2 with 0.16 seconds, both showing robust responses but requiring faster recovery times for stabilization. While the combination of Kalman Filter and PID significantly enhances control accuracy, further improvements are necessary to reduce settling times and ensure greater stability under dynamic conditions. This work contributes valuable insights into advanced control techniques for electric vehicle drivetrains and robotic systems.
Broad Learning System: A Derivation-Based Mathematical Formulation Dimas Chaerul Ekty Saputra; Dyah Putri Rahmawati; Affifah Mutiara Pertiwi; Muhammad Ijaz Shafarin; Kharisma Monika Dian Pertiwi; Thinzar Aung Win; Irianna Futri; Pima Hani Safitri
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.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Understanding Large Language Models: A Review Annastasya Nabila Elsa Wulandari; Purwono Purwono; Alfian Ma’arif; Noorulden Basil; Hamzah M. Marhoon
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
PID Control System on Air Levitation Tube Ghazy, Maulana Irfan; Ma'arif, Alfian; Fauzan, Muhammad Fathan
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.218

Abstract

In the modern technological era, industries continuously seek to improve efficiency, speed, and safety in production processes, with control systems playing a vital role, especially in applications requiring high precision. One such application is air levitation systems, which enable contactless object positioning using controlled airflow. This research focuses on the design and implementation of a Proportional-Integral-Derivative (PID) control system for an air levitation tube, specifically aimed at maintaining the stable position of a ping pong ball within the tube. The system employs an ultrasonic sensor to measure the ball's position and a brushless motor, controlled via an Electronic Speed Controller (ESC), to regulate the airflow. The optimal PID parameters were determined through systematic tuning across various setpoints, yielding Kp = 1, Ki = 0.01, and Kd = 0.7, allowing the system to maintain stability and respond effectively to disturbances. The results demonstrate that the PID control system successfully regulates the ball’s position with minimal oscillation and fast response time. This achievement highlights the viability of PID-controlled air levitation systems not only for industrial use but also as educational tools in engineering and automation training. By integrating hardware and control algorithms into a practical setup, this study contributes to both technological development and enhanced pedagogical approaches in control engineering.
A Review Multiphysics Modeling Techniques for PMSM-Based Electric Vehicle Drives Md. Abu Saleh; Md. Mehedi Hasan Mia; Md. Jasim Uddin; Md. Sumon Ali
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.188

Abstract

The objective of this paper is to review Multiphysics modeling techniques for PMSM-based electric vehicle (EV) drives. Because of its great efficiency, power density, and ability to precisely adjust power, permanent magnet synchronous motors (PMSMs) have emerged as the go-to option for electric vehicle (EV) drives. However, a Multiphysics approach that incorporates mechanical, thermal, electromagnetic, and control system dynamics is necessary to effectively describe PMSM-based EV drives. This paper examines the benefits, drawbacks, and uses of several Multiphysics modelling approaches applied to PMSM-based EV drives. Analytical methods and finite element analysis (FEA) are two examples of electromagnetic modelling techniques that are examined in connection with loss prediction and motor design optimization. While mechanical modelling techniques concentrate on vibration and acoustic noise difficulties, thermal modelling procedures are examined to address heat dissipation and performance reliability. One of the main issues lies in the accurate representation of coupled losses electromagnetic, thermal, and mechanical especially under dynamic operating conditions typical of EVs. To improve the dynamic performance and fault tolerance of PMSM drives, control-oriented modelling techniques are also examined. Co-simulation frameworks that combine these several physical domains are also presented in the review, offering a thorough understanding of practical EV applications. The paper concludes by discussing future research possibilities in Multiphysics modelling for PMSM-based EV drives, with a focus on real-time simulation capabilities, computational efficiency, and artificial intelligence integration.
Trends and Evolution Research on Digital-based Assessment: A Bibliometric Analysis using Scopus Database Zafrullah Zafrullah; Alfian Ma'arif; Astri Wahyuni; Aditya Gusti Mandala Putra
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.317

Abstract

This study aims to map global research trends and the evolution of digital based assessment systems through a bibliometric analysis. Using the PRISMA protocol on the Scopus database, publications from 1977 to 2026 were identified, resulting in 3,725 initial documents, which were filtered based on document type (articles), subject area (computer science), and relevance criteria to obtain a final dataset of 312 studies. Data were analyzed using Bibliometrix (R) and VOSviewer to examine publication trends, collaboration patterns, and keyword evolution. The findings indicate that digital based assessment research has evolved from basic computer based testing toward more adaptive and intelligent systems, with a steady annual growth rate of 2.87%. The results also reveal increasing cross regional collaboration, particularly between European and Asian institutions, along with the dominance of high impact journals such as Computers and Education and Computers in Human Behavior. Furthermore, five main research dimensions were identified: system infrastructure, digital pedagogy, advanced psychometric analytics, virtual learning governance, and user psychological behavior. Recent trends highlight the integration of adaptive testing, machine learning, and deep learning to enhance assessment accuracy and academic integrity; however, challenges related to inclusivity, ethical considerations, and student well being remain significant. Therefore, future research should focus on developing intelligent, ethical, and inclusive assessment systems that balance technological advancement with human centered considerations.
Optimized Hybrid Machine Learning Model for Real-Time Financial Fraud Detection Md Zahidul Islam Sany; Zhang Wubo; Shahin Alam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.332

Abstract

The rapid growth of digital financial transactions has increased the demand for intelligent, scalable, and real-time fraud detection systems capable of identifying fraudulent activities with high accuracy and low latency. This paper proposes an optimized hybrid machine learning framework for real-time financial fraud detection by integrating Random Forest and XGBoost within a weighted soft-voting ensemble classifier. To address the severe class imbalance commonly found in financial datasets, the transaction data were preprocessed using the SMOTE-ENN hybrid resampling technique and Min-Max normalization. Bayesian optimization was employed to tune model hyperparameters and improve generalization while reducing overfitting. The proposed framework was trained and evaluated using stratified data partitioning and 5-fold cross-validation, with performance assessed using Accuracy, Precision, Recall, F1-score, AUC-ROC, and false-negative rate. Experimental results demonstrate that the hybrid ensemble consistently outperforms the individual base classifiers, achieving improved fraud detection capability while maintaining high processing throughput suitable for real-time deployment in a Kafka-based streaming environment. These findings indicate that the proposed framework provides an effective and scalable solution for modern financial fraud detection systems.
Optimized YOLOv11 Architecture for Accurate Multi-Class Vehicle Detection under Real-World Conditions Parag Hossain; Md. Hamim Ferdous; Md. Sabbir Mahmud; Shahin Alam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.316

Abstract

Real-time detection of vehicles is essential for modern transportation systems, traffic surveillance, and autonomous driving technologies. With recent progress in deep learning, object detection models have become more reliable across complex and dynamic environments. This study presents a YOLOv11n (nano variant, 2.59M parameters)-based system designed to accurately detect three key vehicle classes cars, buses, and trucks in real time. Unlike prior YOLO versions, YOLOv11 introduces an improved attention mechanism and anchor-free detection head specifically addressing partially occluded and multi-scale vehicles. A customized dataset (compiled from Open Images v7 and BDD100K, spanning day/night and clear/rain/snow conditions; class distribution: cars 48%, buses 30%, trucks 22%) containing 9,989 training images and 1,998 validation images was used to fine-tune the network. Data augmentation techniques, including Mosaic augmentation, HSV color transformations, and random flips, were applied to enhance model robustness. All experiments were conducted on an RTX 3060 Laptop GPU (6GB VRAM). The trained model achieved strong detection performance, with precision of 0.806, recall of 0.751, mAP50 of 0.830, and mAP50–95 of 0.674 (the 15.6% drop indicates moderate localization errors at stricter IoU thresholds). Inference speed reaches 526 FPS (inference-only) and 263 FPS end-to-end, outperforming YOLOv8n by 3.2% in mAP50. These results highlight YOLOv11n’s ability to balance accuracy and computational efficiency, making it well-suited for real-time applications on resource-limited hardware. The proposed detection framework can support future developments in intelligent traffic management, mobility analytics, and automated road monitoring systems.
PLC-Based Trajectory Control of an Omnidirectional Mecanum-Wheel AGV Using Visual–Inertial Tracking and Grid-Based A* Path Planning Binh-Hau Nguyen; Hang-Ri Nguyen; Phuoc-Duy Nguyen; Trung-Kien Pham; Dang-Khoa Tran; Quoc-Trung Nguyen; Tu-Duc Nguyen; Thi-Ngoc-Thao Nguyen; Thi-Ngoc-Hieu Phu; Hoang-Lam Le; Hoang-Phuc Le; Cong-Tan Bien
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.318

Abstract

This paper presents a PLC-based omnidirectional Automated Guided Vehicle (AGV) using four Mecanum wheels for indoor material transportation. The system integrates a Mitsubishi Q-series PLC for motion execution, an Intel RealSense T265 visual–inertial camera for relative pose tracking, and a Python-based supervisory interface running on a laptop. The AGV operates in a known static workspace represented by a predefined grid map, where each cell corresponds to 0.1 m in the real environment. Static obstacles, pickup points, and drop-off points are manually defined before operation. The A* algorithm generates collision-free waypoint paths, which are converted into wheel velocity commands through the inverse kinematic model and transmitted to the PLC via Ethernet-based MC-Protocol. Experimental tests at a commanded speed of 0.2 m/s show that the prototype can perform eight-directional motion and complete predefined pickup–delivery tasks. The main limitations include tracking drift of the T265 camera, lighting sensitivity, wheel slippage on tiled floors, and communication latency. Future work will focus on sensor fusion, real-time obstacle detection, and fail-safe mechanisms.

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