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Van-Dong-Hai Nguyen
Ho Chi Minh City University of Technology and Engineering
Engineering

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Integrated Vision-PLC Control Architecture for High-Performance Delta Robot Sorting in Industrial Automation Kim-Thanh Vo; Bui-Duc Nghia; Huy-Vu Tran; Thanh-Tuan Huynh; Huy-Bao Nguyen; Phong-Luu Nguyen; Van-Tuan Nguyen; Anh-Quoc Phan; Son-Thanh Phung; Van-Dong-Hai Nguyen; Binh-Hau Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen
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.
ANFIS-Based PD-Fuzzy Control for Pendubot Stabilization Van-Long Mach; Dang-Khoa Huynh; Khanh-Hung Le; Vi-Khang Nguyen; Van-Hai-Dang Ma; Viet-Phi Le; Nguyen-Duy-Phuong Huynh; Le-Nhat Nguyen; Quang-Hoa Le; Van-Dong-Hai Nguyen
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.540

Abstract

The Pendubot is a nonlinear underactuated system with two rotational degrees of freedom and a single actuator, making stabilization a challenging problem for intelligent and nonlinear control methods. Reliable balancing control is important for evaluating control strategies under both simulation and practical hardware constraints. However, although fuzzy and intelligent control methods have been investigated for Pendubot systems, the practical implementation and experimental behavior of ANFIS-based PD-Fuzzy control remain insufficiently documented, particularly in comparison with a conventional PD controller under the same laboratory conditions. This study aims to develop and evaluate an ANFIS-based PD-Fuzzy controller for TOP-position stabilization of a Pendubot. The nonlinear dynamics are formulated using the Euler-Lagrange method, while controllability of the linearized model is examined at the TOP and MID equilibrium points. The proposed controller uses ANFIS-based fuzzy blocks to approximate the proportional control actions, while derivative paths remain explicitly implemented. MATLAB/Simulink simulations show that the baseline PD and PD-Fuzzy controllers produce closely matched TOP-balancing responses, with settling times of approximately 2.36 and 2.37 s for link 1, respectively. Experimental evaluation using an STM32F407-based platform demonstrates practical TOP balancing; however, the baseline PD controller provides more favorable behavior than the PD-Fuzzy realization under the reported hardware conditions. These findings indicate that ANFIS-based PD-Fuzzy control is feasible for Pendubot stabilization but does not necessarily provide performance improvement over a conventional PD controller. The study therefore highlights the importance of hardware-aware tuning, sensor quality, and broader ANFIS training data for future improvements.
Co-Authors Anh-Quoc Phan Binh-Hau Nguyen Bui-Duc Nghia Dang-Khoa Huynh Huy-Bao Nguyen Huy-Vu Tran Khanh-Hung Le Kim-Thanh Vo Le-Nhat Nguyen Nguyen-Duy-Phuong Huynh Phong-Luu Nguyen Quang-Hoa Le Son-Thanh Phung Thanh-Binh Nguyen Thanh-Tuan Huynh Van-Hai-Dang Ma Van-Hiep Nguyen Van-Long Mach Van-Tuan Nguyen Vi-Khang Nguyen Viet-Phi Le