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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Physical Review Journal of Robotics and Control (JRC) Control Systems and Optimization Letters
Perkasa, Sigit Dani
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Physics

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Journal : Control Systems and Optimization Letters

 1 
Audio-Based Telemetry Using HT Radios for Remote Monitoring of Renewable Energy Systems Perkasa, Sigit Dani; Muzadi, Ahmad Rahmad; Megantoro, Prisma; Pandi, Vighneshwaran
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.v3i1.181

Abstract

Effective monitoring of renewable energy systems, such as wind turbines and photovoltaic arrays, is essential for optimizing energy production. However, traditional wired monitoring systems are expensive and lack flexibility. This study develops a reliable wireless monitoring system that addresses the limitations of wired alternatives, using a PZEM-004T power meter, Arduino Uno R3, and BF-888S HT radios. The system employs audio-modulated binary encoding for long-range, low-cost data transmission, enabling real-time monitoring of key power parameters, including voltage, current, and power factor. This solution offers scalability and cost-effectiveness by eliminating the need for extensive infrastructure. The methodology involves designing both hardware and firmware for the transmitter and receiver components and developing a communication algorithm to optimize data transfer efficiency. The system was tested in various environments: indoor, outdoor, and radio communication scenarios. Performance varied across environments, with outdoor and higher-floor tests experiencing more significant interference, which impacted transmission quality. The system achieved an average transmission time of 42.64 seconds and an error rate of 0.56% across 16 channels, demonstrating competitive reliability compared to existing wireless systems. Future research could explore adaptive modulation techniques to enhance data reliability in high-interference environments, improving the system's robustness for large-scale deployments.
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.
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.
Refined Velocity–Position Dynamics in Particle Swarm Optimization: A Survey of Recent Mathematical Innovations Perkasa, Sigit Dani; 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.v3i2.203

Abstract

Particle Swarm Optimization (PSO) remains a pivotal metaheuristic for complex optimization, yet its canonical form faces persistent challenges, including premature convergence and inefficacy in dynamic or high-dimensional landscapes. This survey examines recent advancements in refining PSO’s velocity-position dynamics, emphasizing adaptive mechanisms that enhance exploration-exploitation balance, ensure stability in noisy measurement environments, preserve swarm diversity in discrete search spaces, and maintain robustness under changing problem conditions. Evaluation results on standardized benchmark functions and targeted applications—such as crack detection in bridge structural health monitoring, real-time photovoltaic panel solar tracking, and high-dimensional gene-expression feature selection—demonstrate convergence speeds up to 4-times faster, reliable scaling to over 150 dimensions, and task success rates exceeding 98%. However, these refinements incur moderate runtime overhead and require more intensive hyperparameter tuning, posing challenges for large-scale or real-time deployments. Building on the limitations of static parameter settings and theoretical gaps in dynamic adaptation, the study advocates for future research into hybrid metaheuristic frameworks, automated self-tuning strategies, and rigorous theoretical convergence guarantees. This synthesis bridges mathematical innovation with practical insights, guiding researchers in developing next-generation, self-adaptive PSO variants for contemporary optimization demands.
Gaussian-Process-Augmented Particle Swarm Optimization (GP-PSO): Taxonomy, Survey, and Practitioner Guidance Perkasa, Sigit Dani; Jasmine, Senit Araminta; Fikri, Fachrizal
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.v3i3.217

Abstract

High‐fidelity engineering simulations-Computational Fluid Dynamics, Finite‐Element Analysis and system‐dynamics models-impose prohibitive costs on optimization via traditional metaheuristics. Particle Swarm Optimization (PSO) and Gaussian Processes (GPs) have each shown promise, but canonical PSO suffers from premature convergence and excessive iterations when evaluations are noisy or expensive, and GP‐PSO integrations lack a unifying framework. In this review, we introduce a three‐axis taxonomy (1) surrogate‐integration strategy (e.g. fitness‐function replacement vs. augmentation), (2) acquisition (infill) function (e.g. Expected Improvement vs. Upper Confidence Bound), and (3) fidelity paradigm (e.g. single‐ vs. multi‐fidelity)-to classify and compare recent methods. We survey advances in deep‐kernel and neural‐augmented GPs, sparse‐GP approximations, adaptive retraining mechanisms, and hybrid/transfer‐learning extensions. Benchmark results on synthetic test suites and three real‐world applications (aerodynamic shape design, structural health monitoring, chemical‐process tuning) demonstrate 30–70 % fewer costly evaluations and 20–50 % faster convergence compared to PSO baselines, while maintaining or improving solution quality. From these studies, we distill practitioner guidelines for kernel and acquisition‐function selection, fidelity‐level choices, and reproducibility best practices-emphasizing shared code repositories and hyperparameter logs. Finally, we outline future directions in online surrogate updates, convergence theory under uncertainty, physics‐informed kernels, and standardized community benchmarks.
Co-Authors Abror, Abdul Agustiyanto, Frans R Alfian Pramudita Putra Ama, Fadli Anugrah, Antik Widi Awalin, Lilik Jamilatul Fikri, Fachrizal Hatta, Agus Muhamad Herlambang Setiadi Hidayah, Nayu Nurrohma Jasmine, Senit Araminta Katherin Indriawati Muzadi, Ahmad Rahmad Pandi, Vighneshwaran Prisma Megantoro Syahbani, Muhammad Akbar Usamah, Shofi Afghania Vigneshwaran, Pandi Winarno, Hendra Ari