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Alfian Ma'arif
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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 159 Documents
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Comparative Evaluation of Transfer Function and Detailed Switching Models for a PID-Controlled Single-Phase DC–AC Conversion System Fifin Nugroho; Hari Maghfiroh; Warindi Warindi; Fahmizal Fahmizal
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.v4i2.324

Abstract

Transfer-function (TF) models are widely used in power electronics because of their simplicity and suitability for controller design. However, their ability to represent practical converter behavior is limited since switching actions and nonlinear effects are neglected. This paper presents a comparative evaluation of transfer-function and detailed switching models for a PID-controlled single-phase inverter system consisting of a DC–DC boost converter and a full-bridge inverter employing unipolar sinusoidal pulse width modulation (SPWM). The system is designed to convert a 46 VDC input into a regulated 220 VAC (RMS), 50 Hz output. Both models were developed in MATLAB/Simulink and evaluated under identical operating conditions. The results show that the TF model provides a computationally efficient representation suitable for preliminary controller design, while the detailed switching model captures switching-induced ripple, transient dynamics, and harmonic distortion. The detailed model achieved stable 220 VAC operation with a total harmonic distortion (THD) of 1.91%, satisfying IEEE Standard 519 requirements. The comparative analysis indicates that controller parameters obtained from the TF model can provide a useful initial tuning reference before refinement in the detailed switching model. Since the study is based exclusively on simulation, experimental validation remains necessary. The results suggest that TF models are suitable for early-stage controller development, whereas detailed switching models are required for realistic performance assessment and validation.
Pattern Analysis on Multi-Sensor Networks: Short-Term Forecasting at Parangtritis Coastal, Yogyakarta, Indonesia Haris Imam Karim Fathurrahman; Choirul Fajri; Chin Li-Yi; Khoirudin Wisnu Mahendra
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.v4i2.333

Abstract

Reliable microclimatic records are a prerequisite for evidence-based agricultural planning, yet high-resolution ground-truth datasets for tropical coastal environments remain scarce in the Indonesian literature. This study analyzes six months (December 2025 – May 2026) of 10-minute interval observations across 18 meteorological variables from the Parangtritis Automated Weather Station (AWS), Bantul Regency, Yogyakarta, Indonesia. Rainfall was reconstructed via a differential method from the cumulative station counter, yielding a period total of 1748.4 mm over 180 days. Of these, 112 days (66.1%) recorded measurable precipitation, punctuated by seven dry-spell episodes; the longest extended 12 consecutive days (5–16 May 2026). Schmidt-Ferguson classification returned Q = 20%, placing the site in Climate Type B (Wet/Basah). Reference evapotranspiration (ET₀, Hargreaves–Samani) averaged 10.20 mm/day (total: 1796.0 mm), and a PDSI proxy indicated extreme drought conditions by the close of the observation period (PDSI = −7.45), a deficit attributable primarily to persistently high ET₀ demand rather than rainfall deficiency per se. Cross-correlation analysis identified relative humidity as the dominant concurrent temperature predictor (lag 0; r = −0.676). An XGBoost model achieved short-term temperature forecasting accuracy of MAE = 0.56°C and RMSE = 0.71°C (R² = −1.419, reflecting the site's narrow diurnal thermal variance rather than model failure). Prophet projected sharply reduced rainfall for June–August 2026 (0.25–4.77 mm), which the generic staple-crop suitability framework scores as Not Recommended, reflecting unsuitability for rainfed rice, yet these drier conditions are agronomically favourable for shallot in coastal Yogyakarta.
Robust Parameter Identification and Control Modeling of Low-Cost Brushed DC Motors Using the Nelder-Mead Algorithm Channareth Srun; Mengseu Pheng; Sovathana Um; Chivon Choeung; Seven Siren; Sros Nhek
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.v4i2.321

Abstract

Brushed DC motors are widely used in next-generation automation systems due to their low complexity and ease of control. However, more affordable models often lack sufficient information about their detailed parameters, which makes accurate control and modeling difficult. This paper presents an estimation method for the main parameters of a low-cost brushed DC motor using the Nelder-Mead algorithm. Real-time measurements of speed were obtained through Arduino-based testing, followed by parameter estimation using MATLAB and Simulink. The estimated parameters include armature resistance, inductance, moment of inertia, viscous damping coefficient, back electromotive force constant, and torque constant. The estimated results, validated strictly against a high-specification reference motor datasheet, demonstrate strong accuracy in critical mechanical parameters. Specifically, the algorithm estimated the torque constant with a minimal error of 0.17% and the viscous damping coefficient with an error of 4.8%. However, due to the inherent structural unidentifiability when relying solely on macroscopic speed measurements, electrical parameters such as armature resistance, inductance, and moment of inertia exhibited severe deviations ranging from 39.6% to 52.7%. While the objective function's inability to fully decouple these intertwined variables restricts isolated physical parameter extraction, the method effectively captures the equivalent macroscopic dynamic behavior. The predictive validity of the proposed method was further confirmed by implementing a PI controller based on the estimated transfer function. The experimental results confirm that despite internal physical parameter discrepancies, the algorithm provides an equivalent and robust dynamic model that significantly improves motor performance in control systems. This work proposes an inexpensive and efficient system identification solution for low-cost motor control characterization.
Comparing LQR, SMC, and Backstepping for Active Suspension: Robustness, Energy Efficiency, and Frequency Response Ali Aniss Ebrahim
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.v4i2.319

Abstract

While active suspension systems have advanced significantly, the literature still lacks a systematic compara tive framework that integrates time-domain robustness analysis, frequency-domain vibration isolation, and operational constraints such as actuator saturation. This study presents a comparative framework to bridge this gap through a quantitative evaluation of three advanced control strategies: Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Backstepping. The strategies were evaluated under multiple test scenarios, including: a step signal (0.05 m for 0.2 s), a 0.02 m amplitude sine wave with varying frequencies between 0.5 and 10 Hz, a random wave, ±20% variations in system parameters, and simulated actuator saturation constraints at ±1500 N. The SMC controller demonstrated exceptional robustness under uncertainty, achieving a 34.2% improvement in suspension deflection, while the LQR controller demonstrated superior energy efficiency, outperforming SMC by 28.5%. Frequency response analysis revealed that LQR is optimal in the low frequency band (0–2 Hz), while SMC excels in the mid band (2–8 Hz). Analysis of variance (ANOVA) confirmed statistically significant differences between the strategies (F(2,87) = 24.36, p 0.001). This framework provides a quantitative trade-off model that guides designers to: use SMC for applications requiring high robustness under uncertain conditions (such as vehicles operating on varying terrain), use LQR when energy efficiency is a top priority (such as electric vehicles), and use Backstepping as a compromise that ensures guaranteed mathematical stability with balanced performance.
Interpolation-Based Robust Tracking Control of an LC-Filtered Three-Phase Inverter Vichet Huy; Chivon Choeung; Sovann Ang; Heng Tang; Panha Soth
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.v4i2.334

Abstract

This paper proposes an interpolation-based robust tracking control strategy for an LC-filtered three-phase inverter operating under parameter uncertainties and input constraints. Conventional LMI-based robust controllers are typically synthesized using a single set of controller gains, resulting in a trade-off between transient performance and actuator constraint satisfaction. This work addresses this limitation by introducing an interpolation-based robust control strategy that combines aggressive and conservative controllers through a recursive interpolation algorithm. The proposed method employs two robust tracking controllers synthesized using LMI optimization. The first controller is designed with aggressive feedback gains to achieve fast voltage tracking and superior transient performance, whereas the second controller adopts more conservative gains to enlarge the feasible operating region and ensure input constraint satisfaction. During operation, the feasibility of the control input generated by the tight controller is evaluated first. If the input satisfies the prescribed actuator constraint, the tight controller is applied directly. Otherwise, a recursive interpolation algorithm is activated to generate a feasible control input by combining the outputs of the tight and loose controllers. This strategy preserves the fast dynamic response of the tight controller whenever possible while ensuring that the applied interpolated control input satisfies the prescribed actuator constraint under demanding operating conditions. The effectiveness of the proposed approach is validated through comprehensive MATLAB simulations under nominal and uncertain operating conditions. Simulation results demonstrate the effectiveness of the proposed method for digitally controlled LC-filtered three-phase inverters operating under parameter uncertainties and actuator constraints.
Deep Learning Architectures for Seismic Upgoing–Downgoing Wavefield Separation: A Comparative Benchmark and Cross-Dataset Generalization Study Monirul Islam; Zhong Yu; 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.v4i2.336

Abstract

Separating upgoing (reflection) energy from downgoing (source-side and multiple) energy is a critical preprocessing step in reflection and vertical seismic profile (VSP) seismology, yet classical frequency–wavenumber, median, and Radon-based filters degrade sharply under lateral velocity variation, topography, and spatial aliasing. This paper reports a systematic, same-dataset comparison of seven wavefield-separation algorithms: a simple convolutional network (CNN), U-Net, bidirectional long short-term memory (BiLSTM), Transformer, ResNet, an MLP with PCA dimensionality reduction, and the classical f–k filter trained and evaluated on 201 synthetic acoustic shot gathers and stress-tested on an independent 240-gather cross-dataset. BiLSTM achieved the best in-distribution performance (correlation = 0.9822, SNR = 14.52 dB) and the smallest relative degradation (41.2%) under domain shift, while U-Net was the strongest convolutional architecture (correlation = 0.7600) and the classical f–k filter performed worst (correlation = 0.3003, SNR = −0.37 dB). All models lost substantial accuracy on the cross-dataset, confirming that domain shift not architectural capacity is the principal barrier to field deployment. The study contributes a reproducible, consistently evaluated benchmark; a rigorous cross-dataset generalization test rarely reported in the literature; and quantitative evidence that recurrent and attention-based sequence models outperform convolutional counterparts for 1-D trace-wise wavefield separation. The findings motivate transfer learning, physics-informed regularization, and larger, more diverse training sets as the next steps toward field-ready deployment.
Cross-Coupled PID Control for Dual DC Motors: Simulation and Experimental Evaluation under Dynamic Loads Riky Dwi Puriyanto; Haris Imam Karim Fathurrahman; Efa Wakhidatus Solikhah
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.v4i2.339

Abstract

Synchronization control of multiple DC motors is essential in various industrial applications, including dual-drive systems, gantry mechanisms, and autonomous mobile platforms, where synchronization errors may degrade positioning accuracy and mechanical reliability. Conventional independent PID controllers are unable to compensate for asymmetric disturbances occurring between motors, resulting in synchronization deviations under varying load conditions. This study proposes a Cross-Coupled Control Proportional–Integral–Derivative (CCC-PID) controller integrated with a Kalman Filter to improve synchronization accuracy and disturbance rejection in a dual DC motor system. First, mathematical models of two DC motors were identified using the MATLAB System Identification Toolbox, yielding second-order transfer functions with Best Fit accuracies of 93.11% and 92.78%, respectively. The identified models were employed for controller design and simulation, followed by real-time implementation on a hardware platform. Controller performance was evaluated using the maximum synchronization error (|ε|_max), synchronization recovery time (t_sync), Integral of Time-weighted Absolute Synchronization Error (ITASE), and Tracking-to-Synchronization Ratio (ρ_sync). Simulation results demonstrate that increasing the cross-coupling gain significantly enhances synchronization performance, reducing |ε|_max from 14.44 RPM to 0.34 RPM and ITASE from 7.81 to 0.11. Under dynamic load disturbances, the proposed CCC-PID reduced the maximum synchronization error by 49.8%, decreased ITASE by 86.6%, and shortened the synchronization recovery time from 0.90 s to 0.06 s compared with the conventional PID controller. Experimental validation further confirmed reliable synchronization under static loads of 100–400 g and dynamic loading conditions, while the Kalman Filter effectively suppressed encoder measurement noise, producing smoother RPM feedback and more stable control actions. These results demonstrate that the proposed CCC-PID with Kalman Filter provides accurate synchronization, fast disturbance recovery, and robust operation, making it a practical solution for high-performance dual DC motor synchronization systems.
Scientific Literacy Instruments in Science Education: A Meta-Analytic Study on Internal Consistency Hajidah Salsabila Allissa Fitri; Alfian Ma’arif; Casphama Jovansyah Chaidir; Zafrullah Zafrullah; Nurdin Munthe; Sumalee Chuchai; Shazia Aslam
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.v4i2.338

Abstract

Scientific literacy is a central goal of science education and is commonly assessed using diverse instruments across educational levels and contexts. However, internal consistency estimates are sample dependent and may vary across studies. This study conducted a reliability generalization meta analysis of Cronbach’s alpha coefficients reported for scientific literacy instruments. A systematic search of the Scopus database identified empirical studies reporting Cronbach’s alpha for scientific literacy instruments. In total, 19 studies providing 20 reliability estimates, comprising 10,163 participants, were included. Reliability generalization and moderator analyses were conducted using mixed effects models in jamovi. The pooled Cronbach’s alpha was 0.814 (95% CI [0.776, 0.853]), suggesting high average internal consistency; however, the extreme heterogeneity (I² = 99.42%) indicates that this estimate represents an overall average rather than a stable or universally generalizable reliability value. Moderator analyses showed that education level, instrument type, study design, item group, and geographical region were not significant moderators of reliability, indicating that these characteristics did not explain the substantial variability across estimates. Publication bias assessment suggested a low risk of bias, although sensitivity analysis showed that the pooled estimate was influenced by the exclusion of low alpha values. For instrument developers, these findings emphasize the need to establish reliability evidence for specific target populations and contexts rather than relying on a pooled reliability coefficient. Future research should investigate additional methodological and contextual factors that may explain the substantial heterogeneity.
Real-Time Retail Forecasting and Anomaly Detection Using Hybrid ARIMA and Neural Network Models Khadija Elkattany; Md Mutasim Billah; 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.v4i2.289

Abstract

This paper presents a hybrid machine learning framework that addresses scalability and accuracy challenges in retail inventory management by integrating real-time demand forecasting with anomaly detection, evaluated using Walmart's historical sales data. Traditional approaches face a trade-off: maintaining individual models for each product category is computationally prohibitive, while generalized models often underperform for dissimilar items, resulting in stockouts or overstocking. To address this, we propose a department-level aggregation strategy that balances specificity and generalization, combined with a hybrid methodology: ARIMA for linear trend and seasonality modeling, cubic spline interpolation to capture nonlinear residual patterns, and neural networks for complex interactions. The framework dynamically adjusts predictions using real-time sales streams and applies residual-based anomaly detection with threshold triggers to identify sudden demand spikes or supply disruptions. Experiments on a filtered Walmart dataset (removing returns, canceled orders, and items with 30 days of historical data; 18 months, 15 departments, aggregated from 100,000+ SKUs) indicate an 18% reduction in mean absolute error (MAE) compared to exponential smoothing baselines (MAE: 235.1 ± 32.8 vs. 310.4 ± 28.5), while spline-enhanced neural networks achieve a 24% improvement over standalone ARIMA (MAE: 235.1 vs. 310.4; p 0.01). The anomaly detection module identifies 92% of simulated irregularities with a 7% false-positive rate and F1-score of 0.89. The proposed framework provides three principal advantages: (1) scalable department-level modeling without per-product customization, reducing training time from 2 hours per product to 12 minutes per department (90% improvement); (2) real-time adaptability to fluctuating demand through 6-hour incremental LSTM updates; and (3) cost-efficient inventory optimization through integrated anomaly alerts, validated in a 6-month pilot across 50 Walmart stores showing 19% stockout reduction and 14% overstocking reduction. This work offers a practical blueprint for retailers to enhance forecasting precision, mitigate supply chain risks, and reduce operational costs in volatile markets.

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