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All Journal Journal of Fuzzy Systems and Control (JFSC)
Thi-Ngoc-Thao Nguyen
Ho Chi Minh City University of Technology and Engineering (HCM-UTE)
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Intelligent Control for 2D-Crane System Trung-Son Huynh; Dang-Khoa Dinh; Trong-Bang Tran; Huu-Loc Dang; Dinh-Nguyen-Phuc Le; Hung-Thinh Bui; Hoang-Lam Le; Thanh-Binh Nguyen; Van-Hiep Nguyen; Le-Nhat-Minh Nguyen; Thien-Quoc Dang; Ngoc-Hung Nguyen; Thi-Ngoc-Thao Nguyen; Huynh-Duc Pham; Xuan-Tien Nguyen; Van-Dong-Hai 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.v4i1.350

Abstract

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.
Development of an Automated PCB Inspection, Error Statistics, and Classification System Truong-Nguyen Phan; Thi-Ngoc-Tram Tran; Thanh-Viet Ho; Binh-Hau Nguyen; Minh-Tri Hoang; Hai-Nam Tran; Nhat-Nam Nguyen; Nguyen-Cong-Anh Tran; Le-Huu-Tri Do; Thi-Ngoc-Thao Nguyen; Nam-Long Tran; Duong-Thuan Nguyen; Van-Huy Le; Van-Tuan Nguyen; Huynh-Anh-Tuan Pham
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.v4i1.353

Abstract

In the electronics manufacturing industry, Printed Circuit Boards are critical to electronic devices, and their quality directly affects product performance and reliability. Common assembly defects, such as missing components, misalignment, or wrong parts, must be detected promptly to reduce waste and maintain reputation. In Vietnam, PCB inspection is largely manual, limiting speed, accuracy, and consistency. The system integrates a YOLOv5-based machine vision module for detecting missing and misaligned components, a Siemens S7-1200 PLC for controlling an XY gantry and conveyor system, and a web interface for real-time monitoring. The primary contributions include: a fully integrated cyber-physical prototype suitable for educational and small-scale industrial use; a novel method for component misalignment detection using fiducial-based relative positioning; and seamless communication between vision, control, and HMI modules. Experimental results on two common PCB types, L298N and ULN2003, demonstrate a classification and error detection accuracy of up to 93%. The system achieves a throughput suitable for laboratory and small-batch production, with a positioning accuracy of ±0.5 mm. The system aims to achieve high accuracy, fast processing, and practical applicability in production lines.
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.
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.
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.
Co-Authors Anh-Son Tran Binh-Hau Nguyen Binh-Hau Nguyen Chi-Phat Pham Dang-Khoa Dinh Dinh-Nguyen-Phuc Le Duc-Dat Nguyen Duong-Thuan Nguyen Hai-Nam Tran Hoai-Bao-Nhan Nguyen Hoang-Lam Le Hoang-Nguyen Nguyen Hoang-Sang Luong Hong-Nho Bui Hung-Ky Nguyen Hung-Thinh Bui Huu-Loc Dang Huu-Nhan Nguyen Huynh-Anh-Tuan Pham Huynh-Duc Pham Huynh-Duc-Anh Nguyen Huynh-Khanh-Nam Pham Le-Huu-Tri Do Le-Minh-Tan Nguyen Le-Nhat-Minh Nguyen Manh-Dung Nguyen Minh-Tri Hoang Nam-Long Tran Ngoc-Hung Nguyen Ngoc-Son Vo Nguyen-Cong-Anh Tran Nguyen-Khang Bui Nguyen-Thanh-Loc Tran Nhat-Nam Nguyen Phuoc-Thinh Dang Quang Tran Quang-Thien Nguyen Quang-Thuan Le Quang-Tung Trinh Tan-Huy Tran Tan-Loc Nguyen Tan-Phat Nguyen Thai-Duong Hoang Thai-Hiep Nguyen Thanh-An Cao Thanh-Binh Nguyen Thanh-Binh Phan Thanh-Sang Nguyen Thanh-Viet Ho The-Nhan Nguyen Thi-Hong-Lam Le Thi-Ngoc-Tram Tran Thien-Bao Truong Thien-Quoc Dang Tien-Dat Nguyen Trong-Bang Tran Trung-Son Huynh Truong-Nguyen Phan Van-Dong-Hai Nguyen Van-Hiep Nguyen Van-Huy Le Van-Tuan Nguyen Viet-Tien-Dung Bui Vo-Minh-Khoa Tran Xuan-Tien Nguyen