Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Le, Hoang-Lam
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Design and Implementation of an IoT-Enabled Autonomous Fire-Fighting Robot Using Vision-Based Fire Detection Nguyen, Hoang-Thong; Nguyen, Quoc-Thuan; Tran, Phuoc-Dat; Nguyen, Quang-Khai; Le, Thi-Hong-Lam; Nguyen, Le-Minh-Kha; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Ngoc-Hung; Nguyen, Thi-Ngoc-Thao; Phung, Son-Thanh; Le, Hoang-Lam; Nguyen, Thanh-Toan; Nguyen, Hai-Thanh
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 Nguyen, Quang-Thien; Nguyen, Anh-Huy; Le, Hoang-Linh; Nguyen, Hai-Thanh; Le, Thi-Hong-Lam; Nguyen, Ngoc-Hung; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Thi-Ngoc-Thao; Nguyen, Minh-Tam; Nguyen, Phong-Luu; Le, Hoang-Lam; Phung, Son-Thanh
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.
Intelligent Control for 2D-Crane System Huynh, Trung-Son; Dinh, Dang-Khoa; Tran, Trong-Bang; Dang, Huu-Loc; Le, Dinh-Nguyen-Phuc; Bui, Hung-Thinh; Le, Hoang-Lam; Nguyen, Thanh-Binh; Nguyen, Van-Hiep; Nguyen, Le-Nhat-Minh; Dang, Thien-Quoc; Nguyen, Ngoc-Hung; Nguyen, Thi-Ngoc-Thao; Pham, Huynh-Duc; Nguyen, Xuan-Tien; Nguyen, Van-Dong-Hai
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.
Co-Authors Bui, Hung-Thinh Dang, Huu-Loc Dang, Thien-Quoc Dinh, Dang-Khoa Huynh, Trung-Son Le, Dinh-Nguyen-Phuc Le, Hoang-Linh Le, Thi-Hong-Lam Nguyen, Anh-Huy Nguyen, Hai-Thanh Nguyen, Hoang-Thong Nguyen, Le-Minh-Kha Nguyen, Le-Nhat-Minh Nguyen, Minh-Tam Nguyen, Ngoc-Hung Nguyen, Phong-Luu Nguyen, Quang-Khai Nguyen, Quang-Thien Nguyen, Quoc-Thuan Nguyen, Thanh-Binh Nguyen, Thanh-Toan Nguyen, Thi-Ngoc-Thao Nguyen, Van-Dong-Hai Nguyen, Van-Hiep Nguyen, Xuan-Tien Pham, Huynh-Duc Phung, Son-Thanh Tran, Phuoc-Dat Tran, Trong-Bang