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All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Hoang-Lam Le
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.
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 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.
PLC-Based Trajectory Control of an Omnidirectional Mecanum-Wheel AGV Using Visual–Inertial Tracking and Grid-Based A* Path Planning Binh-Hau Nguyen; Hang-Ri Nguyen; Phuoc-Duy Nguyen; Trung-Kien Pham; Dang-Khoa Tran; Quoc-Trung Nguyen; Tu-Duc Nguyen; Thi-Ngoc-Thao Nguyen; Thi-Ngoc-Hieu Phu; Hoang-Lam Le; Hoang-Phuc Le; Cong-Tan Bien
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.v4i2.318

Abstract

This paper presents a PLC-based omnidirectional Automated Guided Vehicle (AGV) using four Mecanum wheels for indoor material transportation. The system integrates a Mitsubishi Q-series PLC for motion execution, an Intel RealSense T265 visual–inertial camera for relative pose tracking, and a Python-based supervisory interface running on a laptop. The AGV operates in a known static workspace represented by a predefined grid map, where each cell corresponds to 0.1 m in the real environment. Static obstacles, pickup points, and drop-off points are manually defined before operation. The A* algorithm generates collision-free waypoint paths, which are converted into wheel velocity commands through the inverse kinematic model and transmitted to the PLC via Ethernet-based MC-Protocol. Experimental tests at a commanded speed of 0.2 m/s show that the prototype can perform eight-directional motion and complete predefined pickup–delivery tasks. The main limitations include tracking drift of the T265 camera, lighting sensitivity, wheel slippage on tiled floors, and communication latency. Future work will focus on sensor fusion, real-time obstacle detection, and fail-safe mechanisms.
Co-Authors Anh-Huy Nguyen Binh-Hau Nguyen Chi-Phat Pham Cong-Tan Bien Dang-Khoa Dinh Dang-Khoa Tran Dinh-Nguyen-Phuc Le Hai-Thanh Nguyen Hang-Ri Nguyen Hoang-Linh Le Hoang-Phuc Le Hoang-Thong Nguyen Hung-Thinh Bui Huu-Loc Dang Huu-Nhan Tran Huynh-Duc Pham Kieu-Vinh Nguyen Le-Minh-Kha Nguyen Le-Nhat-Minh Nguyen Minh-Nam Phan Minh-Tam Nguyen Ngoc-Hung Nguyen Phong-Luu Nguyen Phong-Nam Huynh Phuoc-Dat Tran Phuoc-Duy Nguyen Quang Tran Quang-Khai Nguyen Quang-Thien Nguyen Quoc-Thuan Nguyen Quoc-Trung Nguyen Son-Thanh Phung Thanh-Binh Nguyen Thanh-Toan Nguyen The-An Nguyen Thi-Hong-Lam Le Thi-Ngoc-Hieu Phu Thi-Ngoc-Thao Nguyen Thien-Quoc Dang Trong-Bang Tran Trung-Kien Pham Trung-Son Huynh Tu-Duc Nguyen Tuan-Minh Truong Van-Dong-Hai Nguyen Van-Hiep Nguyen Van-Phuc Nguyen Van-Thao Nguyen Vi-Cuong Hong Xuan-Tien Nguyen