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Journal of Fuzzy Systems and Control (JFSC)
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29866537     DOI : https://doi.org/10.59247/jfsc.v1i1.24
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Journal of Fuzzy Systems and Control is an international peer review journal that published papers about Fuzzy Logic and Control Systems. The Journal of Fuzzy Systems and Control should encompass original research articles, review articles, and case studies that contribute to the advancement of the theory and application of fuzzy systems and control, and their integration with other technologies, such as artificial intelligence, machine learning, and optimization.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 111 Documents
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Performance Evaluation of Semi-Active Cab Vibration Isolation of a Wheel Loader Using Fractional-Order PID Controller Chi-Huan Canh; Van-Cuong Bui; Van-Quynh Le
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.395

Abstract

Cab vibration in wheel loaders significantly affects operator ride comfort and working performance. Therefore, this paper presents an approach to improving the vibration isolation performance of the cab in a wheel loader system. First, a three-degree-of-freedom dynamic model is established to characterize the vibration behavior. Subsequently, a semi-active cab vibration isolation (SCVI) system is proposed. To generate the semi-active control force, a fractional-order PID (FOPID) controller is developed. Additionally, a conventional PID controller is considered to facilitate a rigorous and comprehensive evaluation of the proposed control strategy. The grey wolf optimization (GWO) algorithm is employed to tune the controller parameters optimally. Finally, the performance of the proposed system is validated through simulations conducted in the MATLAB/Simulink environment. The results indicate that the proposed SCVI system based on FOPID controller reduces the root mean square (RMS) values of seat acceleration (azs), cab acceleration (azc), and cab vibration isolation mount deflection (zcf) by 20.76%, 22.33%, and 48.09%, respectively, compared to the passive cab vibration isolation (PCVI) system, demonstrating a significant improvement in operator ride comfort. These findings contribute to the advancement of cab vibration isolation systems for construction machinery.
Adaptive Fuzzy Load Prioritization for Energy Management in Hybrid Wind-Solar Microgrids Arunava Chatterjee
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.401

Abstract

Hybrid renewable microgrids are increasingly becoming popular among renewable energy generation schemes for small-scale power. However, the intermittent nature of both these sources leads to frequent power imbalances between generation and load demand. Conventional control strategies often rely on battery storage for compensation in these cases or employ advanced controls. This paper presents an adaptive Fuzzy logic-based load prioritization strategy for better energy management in such microgrids. The proposed method adjusts the non-critical loads dynamically based on real-time power availability instead of relying on storage only. A fuzzy-based decision technique is used to determine load shedding levels using inputs such as power mismatch, system voltage deviation, and state-of-charge (SOC) of the battery. The proposed control improves system stability and reduces dependency on battery storage. Suitable simulations backed by laboratory-scale experiments demonstrate that the proposed method improves voltage regulation and minimizes load disruption. It significantly reduces voltage deviation by almost 66%, battery current by 43%, and improves power utilization to 97% compared to conventional control systems.
Dual-Stream MobileNetV2 and Light Mixer Fusion for Robust Weather Classification Muhammad Reza Alfatah; Hadi Santoso
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.406

Abstract

This study proposes an image-based weather classification model designed to accurately recognize various sky conditions, with the aim of providing accessible weather information for elderly individuals and users with visual impairments. While existing lightweight models often struggle to effectively capture both fine-grained local textures and global contextual patterns, this study bridges the gap by proposing a hybrid dual-branch architecture. Specifically, the proposed model utilizes an early spatial feature-level fusion dual-branch architecture. The first branch combines MobileNetV2 with a Feature Pyramid Network (FPN) and incorporates selective attention mechanisms to capture multi-scale features. The second branch, referred to as the Mixer branch, improves visual feature representation through patch embedding and feature mixing techniques. Outputs from both branches are integrated using a fusion layer before being processed by a softmax classifier. The dataset includes five weather categories: cloudy, foggy, rainy, sunny, and sunrise, and is preprocessed through normalization, data augmentation, and partitioning into training, validation, and testing sets. Model training is conducted using TensorFlow and Keras with the Adam optimizer over a two-phase training schedule of 60 epochs (20 epochs for head-only pre-training and 40 epochs for whole-model fine-tuning). The experimental evaluation achieves a test accuracy of 0.975 (97.50%), with precision, recall, and F1-score reaching 0.976, 0.975, and 0.975, respectively, reflecting consistent and reliable classification performance. These results indicate that the proposed model has strong potential for integration with text-to-speech systems to improve accessibility of weather information for users with special needs.
Software-Based Digital PID Control for a Single-Tank Water Level System Quoc-Toan Nguyen; Hai-Duong Nguyen; Anh-Tuan Nguyen; Tan-Khang Nguyen; Quoc-Hung Nguyen; Phuc-Khanh Dang; Truong-Viet Nguyen; Quoc-Bao Nguyen; Xuan-Cuong Le; Van-Hai Nguyen; Huynh-The-Hung Nguyen; Bao-Trung Mai; Phong Luu Nguyen; That-Ngoc-Hai Ton; Tan-Loc Pham; Minh-Tan Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.374

Abstract

The single tank system is one of the fundamental systems in the field of automatic control and is a suitable choice for implementing system control using a PID controller. Nowadays, the single tank system is widely used in laboratories for conducting experiments related to PID control. Due to its low cost, easily available components, simple construction, and ease of observation, the single tank system is an appropriate model for research in automatic control systems. The primary control method applied to the single tank system is the digital PID control method, also known as discrete PID control, the PID parameters (Kp, Kd, Ki) are selected by the trial-and-error method. Therefore, this paper investigates the variation of transient responses when changing the parameters of the PID controller in order to evaluate the model during laboratory implementation. The main objective of this paper is to design a discrete PID controller through simulation and to experimentally investigate its performance on a real single tank system. Experimental results show that the system operates stably, and the pump speed can be adjusted by changing the parameters of the PID controller. The overshoot starts at over 0.2%, the steady-state error is about 0.02 cm, and the settling time is approximately 4.5 to 5 seconds.
Conveyor Speed Control with Fuzzy-PID Minh-Nam Phan; Van-Thao Nguyen; The-An Nguyen; Thi-Ngoc-Thao Nguyen; Thanh-Binh Nguyen; Thi-Hong-Lam Le; Van-Hiep Nguyen; Hoang-Lam Le; Van-Phuc Nguyen; Phong-Nam Huynh; Vi-Cuong Hong; Tuan-Minh Truong; Kieu-Vinh Nguyen; Huu-Nhan Tran
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.379

Abstract

Conveyor belt systems are essential components in industrial automation, but controlling their speed accurately is a significant challenge. Classic PID controllers, while common, struggle to handle the inherent nonlinearities of these systems, such as varying friction and sudden load changes. The adaptive Fuzzy-PID controller, which auto-tunes its parameters, has been proposed as a superior alternative. However, most existing research is limited to software simulations, leaving a gap between theoretical performance and practical, real-world applicability. This paper addresses this gap by presenting the complete design, construction, and experimental verification of a Fuzzy-PID controller implemented on a physical conveyor belt model. The methodology includes system identification via multi-sine input to extract a baseline transfer function, followed by the deployment of the control algorithm on an embedded Arduino Nano microcontroller. Experimental results are presented and directly compared with those of a conventional PID controller to evaluate its performance. Quantitative findings confirm that the embedded Fuzzy-PID controller provides superior performance, reducing the rise time to 0.16 s (from 0.20 s) and significantly decreasing the settling time to 0.56 s (a 48.1% improvement over the PID's 1.08 s). Furthermore, the steady-state error was reduced by 36.4%, demonstrating its superior stability and efficiency in a practical hardware environment and confirming its feasibility for industrial applications.
Design and Implementation of PID and Fuzzy-PID Controllers for Ball-on-Plate Quoc-Khanh Tran; Pham-Minh-Trong Vo; Hoang-Dung Nguyen; Tran-Nhat Dang; Thi-Ngoc-Thao Nguyen; Thi-Hong-Lam Le; Phong-Luu Nguyen; Thanh-Binh Nguyen; Van-Hiep Nguyen; Ngoc-Long Le
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.380

Abstract

This paper presents the design, implementation, and comparative evaluation of a conventional PID controller and a Fuzzy-PID controller for a nonlinear Ball-on-Plate (BoP). The primary objective is to stabilize the ball at a desired position on the plate while achieving a fast transient response and robustness against disturbances. A conventional PID controller is first designed and tuned using the Ziegler–Nichols method. To improve performance under nonlinear conditions, a Fuzzy-PID controller is developed in which fuzzy logic adaptively adjusts the PID gains online. The proposed controllers are evaluated through three stages: numerical simulation in MATLAB/Simulink, real-time implementation in Python, and experimental validation on a physical hardware platform. Compared with the conventional PID controller, the Fuzzy-PID controller achieves a reduction in maximum overshoot from 0.17% to below 0.1% in simulation, a shorter settling time (approximately 4.0 s for PID versus 2.8 s for Fuzzy-PID), and a reduction in steady-state positioning error of approximately 33–36% in hardware experiments (from ~3–14 pixels to ~2–9 pixels).
Comparative Evaluation of Fuzzy Logic, Sliding Mode, and LQR Controllers for DC Motor Position Control Minh-Thy Pham; Lam-Trong-Tuan Bui; Thi-Thanh-Hoang Le; Van-Bac Nguyen; Le-Khoi-Nguyen Cao; Le-Nhat-Minh Tran; Xuan-Manh Ngo; Phong-Luu Nguyen; Dinh-Phu Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.405

Abstract

This paper presents a comparative evaluation of three advanced control strategies for DC motor position control, namely Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), and Linear Quadratic Regulator (LQR). First, the mathematical model of the DC motor is derived from the electrical and mechanical dynamic equations. Based on this model, the three controllers are designed and implemented in MATLAB/Simulink and experimentally validated on a microcontroller-based platform under identical operating conditions. The comparative analysis is performed using quantitative performance indices, including settling time, overshoot, steady-state error, and control effort. Simulation and experimental results show that the SMC controller provides the best overall performance with fast convergence, high robustness, and small steady-state error, while the FLC approach achieves smoother responses with moderate transient performance. The LQR controller demonstrates rapid state regulation but produces larger transient peaks and higher control effort compared with the other methods. The results highlight the practical trade-offs among intelligent, robust, and optimal control strategies for low-cost DC motor position control applications.
Hybrid GA-GWO with Dual-Vector Encoding for Indonesian School Timetabling Akbar Muhammad Sadat; Alqis Rausanfita; Pima Hani Safitri
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.418

Abstract

School timetabling is a complex combinatorial optimization problem that involves assigning subjects, teachers, and classes to predefined time slots while satisfying numerous institutional constraints. In many Indonesian junior high schools, scheduling is still performed using manual approaches, which are often time-consuming and prone to conflicts. Compared with university timetabling, school timetabling presents additional challenges due to fixed class groups, rigid subject allocations, teacher availability constraints, and institutional regulations. To address these challenges, this study proposes a hybrid optimization framework that combines a Guided Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) for the school timetable. The proposed framework incorporates dual-vector solution encoding to provide a structured representation of scheduling components and support efficient constraint handling during the optimization process. In addition, a majority-voting and guided mutation strategy is employed to enhance the balance between exploration and exploitation. The proposed method was evaluated using real-world scheduling data from an Indonesian junior high school consisting of 27 classes, 54 teachers, 13 subjects, and 36 time slots. Experimental results show that the proposed hybrid GA-GWO achieved a fitness improvement of 95.84%, reducing the fitness value from 16,120 to 670, compared with improvements of 89.83% and 94.31% obtained by Traditional GA and Guided GA, respectively. Although the proposed method required approximately 28 minutes of execution time, it produced the highest overall timetable quality among the evaluated approaches. These findings demonstrate that the integration of dual-vector encoding, majority voting, and guided mutation within a hybrid GA-GWO framework can effectively improve timetable optimization for real-world Indonesian school scheduling environments.
A Multi-Channel Wavelet Long Short-Term Memory Framework for Early Wheel Slip Detection in Urban Light Rail Transit Yulian Lingga Permana; Muhammad Hamka Ibrahim; Hari Maghfiroh
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.419

Abstract

This paper presents an integrated framework combining Continuous Wavelet Transform (CWT) with multi-stream Long Short-Term Memory and Multi-Layer Perceptron (LSTM+MLP) networks for early wheel slip detection in urban Light Rail Transit (LRT). Conventional Wheel Slip Protection (WSP) systems rely on static thresholds applied to axle speed differences, resulting in detection latency that impacts wheel-rail wear and operational disruptions. Three methodological contributions are proposed: (i) a five-channel CWT feature extraction strategy that leverages the structural hierarchy of LRT trainsets consisting of motor car with cabin (MC), motor car (M), and trailer car (T); (ii) a conventional ground truth labeling workflow using Slip/Slide Status signals as a reference, eliminating expert annotation bias; and (iii) the first distance-based spatial slip hotspot mapping integrated with model prediction for the Jakarta LRT line. The framework is validated on a 50-minute operational dataset consisting of 93,755 time steps with a resolution of 32 ms, collected from the LRT and synchronized via Dynamic Time Warping using 47 Train Control and Management System (TCMS) variables with three different sampling rates. Statistical analysis confirms that the motor-trailer speed difference channel achieves a Cohen's d of 1.615 with a Kolmogorov-Smirnov p < 0.001 test between normal and slip distributions. The integrated LSTM+MLP model achieves an F1 score of 0.9272, an AUC of 0.9958, and a median early detection margin of 1.152 seconds before conventional WSP activation, with 100% of test events detected earlier than the conventional system. Complementary spatial mapping derived from a larger multi-train dataset containing 5,948 events identified 414 high-risk zones along the LRT track, supporting predictive WSP threshold modulation and maintenance scheduling that takes rail wear into account.
Comparative Analysis of Random Forest and LSTM for Predictive Maintenance of Electric Motors Muhammad ‘Atiq; Musab Ali El Nefati; Arief Marwanto; Fajar Husain Asy'ari; Rio Subandi; Danang Hendrawan
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.421

Abstract

This study presents a comparative analysis of Random Forest and Long Short-Term Memory (LSTM) algorithms for predictive maintenance of electric motors using Industrial Internet of Things (IIoT) sensor data (current, temperature, vibration). A synthetic dataset from Kaggle comprising 5,000 samples across three operational states (HEALTHY, WARNING, CRITICAL) was employed. Experiments were conducted on Google Colab with a Tesla T4 GPU. The LSTM model achieved a classification accuracy of 98.9%, outperforming Random Forest (98.5%), with perfect precision and recall for the WARNING state and improved recall for the CRITICAL state (0.97 vs. 0.96). However, Random Forest demonstrated substantially shorter training time (2 seconds vs. 30 seconds). For real-time industrial deployment on Programmable Logic Controllers (PLCs), Random Forest is recommended due to its rapid inference (5-10 ms), while LSTM is better suited for critical assets where detection accuracy is prioritized. The findings also confirm the viability of Google Colab as an accessible platform for predictive maintenance research in academic settings. This work contributes a practical framework for selecting between machine learning and deep learning approaches in IIoT-based motor fault diagnosis.

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