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All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Ngoc-Hung Nguyen
Ho Chi Minh City University of Technology and Engineering (HCM-UTE)
Aerospace Engineering Automotive Engineering Computer Science & IT 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.
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.
Design and Implementation of an IoT-Enabled Autonomous Fire-Fighting Robot Using Vision-Based Fire Detection Hoang-Thong Nguyen; Quoc-Thuan Nguyen; Phuoc-Dat Tran; Quang-Khai Nguyen; Thi-Hong-Lam Le; Le-Minh-Kha Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Ngoc-Hung Nguyen; Thi-Ngoc-Thao Nguyen; Son-Thanh Phung; Hoang-Lam Le; Thanh-Toan Nguyen; Hai-Thanh Nguyen
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.354

Abstract

This paper presents the design and implementation of an IoT-enabled autonomous fire-fighting mobile robot for early hazard detection, remote monitoring, and emergency response. The proposed system integrates real-time deep learning–based fire detection using a YOLO model with fire and gas sensor–based monitoring for IoT-based alert transmission and SLAM-based environmental visualization to form a multifunctional robotic platform capable of performing a sequence of tasks from detection and warning to initial fire response. The robot is capable of autonomous movement with obstacle avoidance, while a 2D SLAM-based mapping module is employed to provide environmental visualization for monitoring and decision support. A mobile application enables remote supervision and control, and real-time alerts are delivered through an IoT platform to enhance situational awareness. Experimental results show that the proposed system achieves a fire detection and response success rate of approximately 70%, with reliable fire recognition and fast response time under indoor testing conditions. The developed robot demonstrates strong potential as a practical solution for improving safety and supporting early-stage fire response in residential and industrial environments.
Trajectory Tracking Controller Design for a One-degree-of-Freedom Robotic Arm using Fuzzy Logic and Neural Controllers Quang-Thien Nguyen; Anh-Huy Nguyen; Hoang-Linh Le; Hai-Thanh Nguyen; Thi-Hong-Lam Le; Ngoc-Hung Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Thi-Ngoc-Thao Nguyen; Minh-Tam Nguyen; Phong-Luu Nguyen; Hoang-Lam Le; Son-Thanh Phung
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.271

Abstract

The one-degree-of-freedom (1-DOF) robotic arm is a fundamental platform widely used in laboratories for teaching and evaluating position and trajectory control strategies. This paper presents the modeling, simulation, and experimental implementation of a 1-DOF robotic arm system using intelligent control approaches. A Fuzzy Logic Controller (FLC) and a neural network controller (NNC) based on a multi-layer perceptron (MLP) were designed and evaluated in MATLAB/Simulink and implemented in real time on an STM32F4 embedded hardware platform. Both controllers were tested under step and sinusoidal reference inputs, achieving tracking errors below 5°, settling times of approximately 0.1 s (within ±2%), and limited overshoot. Although the neural network successfully reproduced the general control behavior of the FLC, the fuzzy controller demonstrated slightly smoother responses and lower control effort under multi-level step conditions. A primary contribution of this work is the development and validation of a low-cost STM32F4G-based embedded platform for implementing and experimentally evaluating intelligent control algorithms, providing a practical and scalable solution for intelligent control research and laboratory education in universities.
Co-Authors Anh-Huy Nguyen Binh-Hau Nguyen Chi-Phat Pham Dang-Khoa Dinh Dinh-Nguyen-Phuc Le Hai-Thanh Nguyen Hoang-Lam Le Hoang-Linh Le Hoang-Thong Nguyen Hung-Thinh Bui Huu-Loc Dang Huynh-Duc Pham Le-Minh-Kha Nguyen Le-Nhat-Minh Nguyen Minh-Tam Nguyen Phong-Luu Nguyen Phuoc-Dat Tran Quang Tran Quang-Khai Nguyen Quang-Thien Nguyen Quoc-Thuan Nguyen Son-Thanh Phung Thanh-Binh Nguyen Thanh-Toan Nguyen Thi-Hong-Lam Le Thi-Ngoc-Thao Nguyen Thien-Quoc Dang Trong-Bang Tran Trung-Son Huynh Van-Dong-Hai Nguyen Van-Hiep Nguyen Xuan-Tien Nguyen