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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 138 Documents
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AI-Driven Threat Intelligence on Blockchain Using Deep Learning for Decentralized Cyber Risk Prediction Zangana, Hewa Majeed; Beitollahi, Hakem; Muhamad, Sabat Salih; Mohammed, Aquil Mirza; Wani, Sharyar
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.v3i3.262

Abstract

The increasing complexity of cyber threats such as advanced persistent threats (APTs), ransomware, distributed denial-of-service (DDoS), and smart contract exploits requires cybersecurity solutions that go beyond traditional centralized defenses. This paper proposes an AI-driven threat intelligence framework integrated with blockchain technology for decentralized and trustworthy cyber risk prediction. The novelty of the proposed framework lies in its hybrid architecture, where deep learning–based anomaly detection models (including LSTM and autoencoder networks) analyze real-time cybersecurity data—such as blockchain transaction logs, network activity records, and external threat intelligence feeds—while blockchain is used to securely store, validate, and share AI-generated threat intelligence in a tamper-resistant and decentralized manner. Unlike AI-only solutions that suffer from data integrity and trust issues, or blockchain-only approaches that lack intelligent threat detection, the proposed framework combines the strengths of both technologies to enhance detection accuracy and stakeholder trust. Experimental evaluation conducted in a simulated blockchain environment demonstrates a detection accuracy of 96.4%, a false positive rate of 3.6%, and effective identification of multiple attack categories, including smart contract exploits and 51% attacks. While the framework improves security and transparency for inter-organizational security teams, enterprise networks, and supply-chain partners, it also introduces challenges related to computational overhead and blockchain scalability. Overall, the results indicate that integrating AI-driven threat intelligence with blockchain offers a practical and robust solution for decentralized cybersecurity risk prediction.
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.
Trajectory Tracking Controller Design for a One-degree-of-Freedom Robotic Arm using Fuzzy Logic and Neural Controllers Nguyen, Quang-Thien; Nguyen, Anh-Huy; Le, Hoang-Linh; Nguyen, Hai-Thanh; Le, Thi-Hong-Lam; Nguyen, Ngoc-Hung; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Thi-Ngoc-Thao; Nguyen, Minh-Tam; Nguyen, Phong-Luu; Le, Hoang-Lam; Phung, Son-Thanh
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.v4i1.271

Abstract

The one-degree-of-freedom (1-DOF) robotic arm is a fundamental platform widely used in laboratories for teaching and evaluating position and trajectory control strategies. This paper presents the modeling, simulation, and experimental implementation of a 1-DOF robotic arm system using intelligent control approaches. A Fuzzy Logic Controller (FLC) and a neural network controller (NNC) based on a multi-layer perceptron (MLP) were designed and evaluated in MATLAB/Simulink and implemented in real time on an STM32F4 embedded hardware platform. Both controllers were tested under step and sinusoidal reference inputs, achieving tracking errors below 5°, settling times of approximately 0.1 s (within ±2%), and limited overshoot. Although the neural network successfully reproduced the general control behavior of the FLC, the fuzzy controller demonstrated slightly smoother responses and lower control effort under multi-level step conditions. A primary contribution of this work is the development and validation of a low-cost STM32F4G-based embedded platform for implementing and experimentally evaluating intelligent control algorithms, providing a practical and scalable solution for intelligent control research and laboratory education in universities.

Page 14 of 14 | Total Record : 138

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