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p-Index From 2021 - 2026
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All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters Scientific Journal of Engineering Research
Nguyen, Van-Hiep
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Integrated Vision-PLC Control Architecture for High-Performance Delta Robot Sorting in Industrial Automation Vo, Kim-Thanh; Nghia, Bui-Duc; Tran, Huy-Vu; Huynh, Thanh-Tuan; Nguyen, Huy-Bao; Nguyen, Phong-Luu; Nguyen, Van-Tuan; Phan, Anh-Quoc; Phung, Son-Thanh; Nguyen, Van-Dong-Hai; Nguyen, Binh-Hau; Nguyen, Van-Hiep; Nguyen, Thanh-Binh
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.337

Abstract

The rapid development of automation and robotics has increased the demand for high-performance industrial systems, in which Delta robots play a crucial role due to their lightweight structure, high speed, and precise positioning capability. This study aims to design, implement, and evaluate a Delta robot-based product classification system integrating PLC S7-1200 control and Machine vision. The proposed system employs a camera to detect object shape, color, and position on a conveyor, while a PC processes the image data and computes the robot’s inverse kinematics before transmitting control commands to the PLC. A hardware model of the Delta robot was designed and fabricated, and a dual-mode control application was developed to monitor and operate the robot in real time. Experimental results demonstrate that the system achieves stable operation, with a classification speed of up to 20 products per minute and an accuracy of approximately 95.7% for picking and placing tasks. The findings confirm the feasibility and effectiveness of integrating vision-based detection with high-speed parallel robot control for industrial sorting applications. The study also provides a foundation for further optimization in processing speed, mechanical design, and advanced image-processing techniques to enhance system performance in practical manufacturing environments.
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

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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.
Real-Time Trajectory Tracking Control of a DC Motor Using a Self-Tuning Regulator with Online Parameter Estimation Nguyen, Quang-Thien; Le, Hoang-Linh; Nguyen, Anh-Huy; Nguyen, Duc-Anh-Quan; Nguyen, Van-Dong-Hai; Nguyen, Minh-Tam; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Phuong-Quang; Le, Thi-Hong-Lam; Nguyen, Binh-Hau; Vu, Dinh-Minh
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.v4i1.270

Abstract

An adaptive Self-Tuning Regulator (STR) is developed for DC motor control to address performance degradation caused by load disturbances and parameter uncertainties. The method combines online system identification using recursive least squares (RLS) with automatic controller retuning in discrete time. The motor dynamics are continuously estimated and used to update the controller parameters through a pole-placement (or minimum-variance) design, thereby maintaining the desired closed-loop response without manual gain adjustment. The STR is implemented in real time and tested under speed reference changes and varying load torque. Results confirm that the proposed approach enhances tracking performance and disturbance rejection compared with conventional fixed-gain control, making it suitable for practical DC drive systems operating under changing conditions.
Co-Authors Huynh, Thanh-Tuan Le, Hoang-Lam Le, Hoang-Linh Le, Thi-Hong-Lam Nghia, Bui-Duc Nguyen, Anh-Huy Nguyen, Binh-Hau Nguyen, Duc-Anh-Quan Nguyen, Hai-Thanh Nguyen, Hoang-Thong Nguyen, Huy-Bao Nguyen, Le-Minh-Kha Nguyen, Minh-Tam Nguyen, Ngoc-Hung Nguyen, Phong-Luu Nguyen, Phuong-Quang 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-Tuan Phan, Anh-Quoc Phung, Son-Thanh Tran, Huy-Vu Tran, Phuoc-Dat Vo, Kim-Thanh Vu, Dinh-Minh