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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 99 Documents
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An Enhanced PID-Based Motion Control Framework for Autonomous Line-Following Robot Nguyen-Thanh-Loc Tran; Viet-Tien-Dung Bui; Hong-Nho Bui; Hoang-Nguyen Nguyen; Thi-Ngoc-Thao Nguyen; Thanh-Sang Nguyen; Hung-Ky Nguyen; Huynh-Duc-Anh Nguyen; Thanh-Binh Phan; Hoang-Sang Luong; Le-Minh-Tan Nguyen; Vo-Minh-Khoa Tran; Tien-Dat Nguyen; Huynh-Khanh-Nam Pham; Duc-Dat Nguyen; The-Nhan Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.368

Abstract

PID controller is widely used in automatic control systems because it is simple, reliable, and easy to apply. It is especially suitable for mobile robots, such as line-following robots. The main contribution of this work is an experimental method to tune PID parameters. Instead of using complex algorithms, the parameters are adjusted and tested directly on a real robot. This makes the method easier to apply, especially for low-cost and educational systems. Experiments were conducted to evaluate how PID parameters (Kp, Ki, and Kd) affect the robot’s performance. The robot was tested on different paths, including straight lines, curves, and 90-degree turns. The results show that the optimal parameters are Kp = 65, Ki = 0.1, and Kd = 13. With these values, the robot moves smoothly, responds quickly, and follows the path accurately.
Fuzzy-PID Control for Balancing a Two-Wheeled Inverted Pendulum Robot Fahmizal; Afrizal Mayub; Lintang Fathia Rafanah; Smail Latifa
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.390

Abstract

A two-wheeled inverted pendulum robot (TWIPR) requires continuous control action because its upright position is inherently unstable and highly sensitive to disturbances. This research proposes the use of a fuzzy-PID controller to keep the TWIPR balanced. Although PID has several advantages, its performance can degrade when the system is subjected to changing conditions. To address this, fuzzy logic is applied to enhance the adaptive capabilities of the PID controller. The fuzzy system dynamically generates PID parameters based on predetermined fuzzy rules, effectively maintaining system stability. The fuzzy membership functions used, namely MF3, MF5, and MF7, were compared through no-load and loaded tests. In the no-load test, the fuzzy-PID with MF7 reduced rise time, settling time, overshoot, peak value, and peak time by 1.229%, 0.673%, 86.703%, 7.232%, and 2.952%, respectively, compared with those of the conventional PID. However, the MF3 configuration only excels in overshoot and peak time, while the MF5 configuration only shows improvements in settling time, overshoot, and peak value. Further testing results show that Fuzzy-PID with MF7 provides the most stable performance under load conditions.
Hybrid Predictive Fuzzy-AI Energy Management with SOC-Constrained Optimization for Wind-Driven Microgrids Arunava Chatterjee
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.398

Abstract

Small-scale wind energy systems are increasingly being installed in distributed renewable systems and microgrids. These systems display significant power variability due to the uncertain nature of wind speed. Such fluctuations often lead to DC-link voltage deviations and irregular battery charging cycles. Thus, they can reduce overall system reliability and storage lifespan. This paper proposes a hybrid predictive Fuzzy-AI supervisory control strategy for energy management in a wind-battery microgrid. The supervisory layer integrates short-term wind power forecasting with fuzzy logic-based battery scheduling while enforcing state-of-charge (SOC) constraints. A multi-objective formulation is adopted to regulate DC-link voltage and simultaneously minimize battery current stress. The proposed controller generates adaptive battery current references through a rule-based inference mechanism with predictive information. Comparative results with conventional PI control and AI-only scheduling demonstrate improved voltage stability, reduced RMS battery current, and better SOC control. Experimental observations obtained from a small-scale wind generation setup further support the effectiveness of the proposed approach.
A Study of a Laser Engraving System Based on a Cartesian Robot with Image Processing Thai-Duong Hoang; Manh-Dung Nguyen; Chi-Phat Pham; Thi-Ngoc-Thao Nguyen; Tan-Phat Nguyen; Anh-Son Tran; Quang-Thuan Le; Phuoc-Thinh Dang; Thai-Hiep Nguyen; Quang-Tung Trinh; Hoai-Bao-Nhan Nguyen; Huu-Nhan Nguyen; Thanh-Binh Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.361

Abstract

Traditional CNC laser engraving systems often face limitations in flexibility, requiring manual alignment and pre-defined G-code files. This paper proposes an advanced automated laser engraving system based on a 3-axis Cartesian robot that bridges the gap between industrial control reliability and modern computer vision. The core novelty of this research lies in the seamless integration of a Mitsubishi Q03UDE Programmable Logic Controller (PLC) with a Python-based image processing framework. By utilizing the OpenCV library for real-time edge detection and trajectory generation, the system can autonomously identify object positions and convert complex patterns into precise motion commands. Communication is established via the MC Protocol over Ethernet, ensuring high-speed data synchronization between the vision system and the servo-driven hardware. Experimental results demonstrate that the proposed system achieves high precision in engraving, significantly reduces setup time by eliminating manual calibration, and maintains the robust stability required for industrial environments. This approach provides a scalable solution for intelligent manufacturing and personalized production.
Driver Drowsiness Detection and Warning System Using Computer Vision and Neural Networks on Embedded Platforms Chi-Phat Pham; Quang Tran; Binh-Hau Nguyen; Van-Dong-Hai Nguyen; Thi-Hong-Lam Le; Ngoc-Hung Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Thi-Ngoc-Thao Nguyen; Hoang-Lam 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.v4i2.372

Abstract

Driver drowsiness is one of the leading causes of traffic accidents worldwide. Traditional monitoring approaches, such as vehicle-based parameter analysis or physiological signal measurement, often require intrusive sensors or deep access to vehicle systems. To overcome these limitations, this paper proposes a real-time driver drowsiness detection and warning system using computer vision combined with a neural network classifier on an embedded platform. Facial landmarks are extracted using the dlib 68-point model, and the Eye Aspect Ratio (EAR) is computed to evaluate eye-closure behavior. A deep neural classifier is trained on eye-state and temporal EAR sequences collected from 25 subjects to classify normal and drowsy conditions. The system is deployed on a Raspberry Pi 3 B+ embedded platform, integrated with an Arduino-based alarm module to deliver audio–visual alerts when drowsiness is detected. Experimental results demonstrate a training accuracy of 98.4% and a testing accuracy of 92.8% with real-time performance of 15–20 FPS under daylight conditions, stable performance in real time, and feasibility for installation in passenger cars, trucks, and buses. The proposed method contributes a low-cost, efficient, and deployable solution for reducing road accidents with a focus on lightweight embedded implementation.
Development of an AI and Webserver-integrated Smart Automated Storage and Retrieval System Quang-Thien Nguyen; Thien-Bao Truong; Tan-Huy Tran; Tan-Loc Nguyen; Ngoc-Son Vo; Nguyen-Khang Bui; Van-Dong-Hai Nguyen; Thanh-An Cao; Thi-Ngoc-Thao Nguyen; Thi-Hong-Lam 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.v4i2.381

Abstract

In recent years, Automated Storage and Retrieval Systems (AS/RS) and their development have been a notable trend of modern warehouse management by automating the sequential and precise processes of storing, sorting, and retrieving goods. Driven by the convergence of mechatronic systems, Industrial Internet of Things (IIoT), Artificial Intelligence (AI), cloud storage, and edge-based management systems, the potential and practical benefits of AS/RS can be significantly amplified when effectively combined with these trends. In this field, although some works are presented, they often lack specialization for the Vietnamese industrial environment and sustainability. Therefore, this research presents the development of an intelligent AS/RS, incorporating AI-based label processing and webserver-based control to enhance warehouse management efficiency. Experimental evaluations demonstrate that the system achieves high reliability in product classification and storage tasks, providing a scalable solution for modern smart logistics with real-time data synchronization capabilities via a Node-RED web server.
The Causal-Entropic Fuzzy Inference: A Bayesian Framework for Explainable and Robust Reasoning Jin-Hyok Choe; Yon-Ju Jang; Son-Il Kwak; Ok-Sim Ri; Hyon-U Kong
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.392

Abstract

Traditional fuzzy reasoning methods exhibit limitations in satisfying the reductive property and handling uncertain environments. This paper proposes a novel Causal-Entropic Fuzzy Inference (CEFI) framework that integrates causal discovery with Bayesian inference to overcome these limitations. The proposed method consists of three main components: (1) a causal rule discovery mechanism based on conditional independence tests, (2) an entropic inference engine utilizing variational free energy minimization, and (3) an active perception module for strategic information gathering. Experimental results on SISO and MISO systems demonstrate that CEFI achieves 99.4% reductive property, outperforming state-of-the-art methods by 7.3-31.2% in noisy environments while providing causal explanations for reasoning processes.
Fault Tolerant Control of Robot Manipulator with Actuator Effectiveness Adaptation Anisa Ulya Darajat; Swadexi Istiqphara; Heriansyah; Mohammad Farhan Ferdous; Abu Saleh Musa Miah; Uri Arta Ramadhani; Hari Maghfiroh
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.397

Abstract

This study discusses the implementation of fault-tolerant control (FTC) for a planar two-degree-of-freedom (2DoF) robot manipulator experiencing actuator loss of effectiveness. Several methods have been proposed, such as PID and MRAC; however, their accuracy still needs improvement. Meanwhile, FT-SMC offers high accuracy, but its methodological complexity results in longer execution time and reduced computational efficiency. The objective of this research is to develop a fault-tolerant control method that can maintain system performance under actuator degradation while achieving high tracking accuracy with improved computational efficiency. Simulations are performed with a two-link manipulator model with sinusoidal reference trajectories. An actuator fault is introduced at 4 s by reducing the actuator effectiveness to [0.5, 0.7]ᵀ, meaning that the actuator capability decreases to 50% and 70% of its nominal performance, respectively. The simulation results show that the proposed FTC controller maintains good tracking performance after the fault occurs. In contrast, the controller without FTC experiences performance degradation characterized by phase lag and amplitude attenuation in the system response. Furthermore, the actuator effectiveness estimation mechanism demonstrates fast convergence after the fault occurs, with settling times of approximately 0.084 s and 0.238 s for the first and second joints, respectively. The steady-state MAEs are 0.0080 and 0.0395, equivalent to relative errors of 1.6% and 5.6%, respectively. Compared with other FTC methods, the proposed FTC controller also provides a balanced trade-off between tracking accuracy, robustness under fault conditions, and computational efficiency, making it suitable for real-time implementation.
Imbalance Handling Strategies for Predictive Maintenance Under Leakage-Free Factorial Evaluation Tedy Rismawan; Irma Nirmala
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.399

Abstract

Predictive maintenance (PdM) in industrial manufacturing relies on machine learning classifiers trained on severely imbalanced sensor data, where failure events represent a small minority of observations. This study presents a controlled factorial experiment evaluating five algorithms (Decision Tree, Random Forest, SVM, XGBoost, and Logistic Regression) against four imbalance handling strategies (no handling, SMOTE, ADASYN, and class weighting) across binary and six-class failure mode identification tasks on the AI4I 2020 dataset (10,000 observations, 3.39% failure rate), yielding 40 experimental conditions. All oversampling steps were integrated within an ImbPipeline to prevent data leakage across cross-validation folds. Statistical comparisons were conducted via the Friedman test, post-hoc Nemenyi analysis, and one-tailed Wilcoxon signed-rank tests. XGBoost with no handling achieved the highest performance in both tasks (binary F1 = 0.8952; multiclass F1 = 0.6084). Contrary to common practice, no handling method outperformed SMOTE or ADASYN across four of five algorithms in the binary task (Wilcoxon, p = 0.0312), while class weighting improved macro recall from 0.8448 to 0.8908 without significant F1 degradation. Per-class analysis showed that heat dissipation, power, and overstrain failures were reliably detected (F1 > 0.82), while tool wear and random failures remained undetectable. In the multiclass task, ADASYN and XGBoost class weighting were replaced by SMOTE due to instability with extreme minority classes. These findings demonstrate that synthetic oversampling is not universally beneficial for imbalanced PdM data, and that leakage-free experimental design is essential for reliable performance estimation. Practitioners are advised to benchmark no handling and class weighting before applying synthetic oversampling in PdM deployments.

Page 10 of 10 | Total Record : 99

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