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All Journal Journal of Fuzzy Systems and Control (JFSC)
Phong Luu Nguyen
Ho Chi Minh City University of Technology and Engineering (HCM-UTE)
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Software-Based Digital PID Control for a Single-Tank Water Level System Quoc-Toan Nguyen; Hai-Duong Nguyen; Anh-Tuan Nguyen; Tan-Khang Nguyen; Quoc-Hung Nguyen; Phuc-Khanh Dang; Truong-Viet Nguyen; Quoc-Bao Nguyen; Xuan-Cuong Le; Van-Hai Nguyen; Huynh-The-Hung Nguyen; Bao-Trung Mai; Phong Luu Nguyen; That-Ngoc-Hai Ton; Tan-Loc Pham; Minh-Tan Nguyen
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.v4i2.374

Abstract

The single tank system is one of the fundamental systems in the field of automatic control and is a suitable choice for implementing system control using a PID controller. Nowadays, the single tank system is widely used in laboratories for conducting experiments related to PID control. Due to its low cost, easily available components, simple construction, and ease of observation, the single tank system is an appropriate model for research in automatic control systems. The primary control method applied to the single tank system is the digital PID control method, also known as discrete PID control, the PID parameters (Kp, Kd, Ki) are selected by the trial-and-error method. Therefore, this paper investigates the variation of transient responses when changing the parameters of the PID controller in order to evaluate the model during laboratory implementation. The main objective of this paper is to design a discrete PID controller through simulation and to experimentally investigate its performance on a real single tank system. Experimental results show that the system operates stably, and the pump speed can be adjusted by changing the parameters of the PID controller. The overshoot starts at over 0.2%, the steady-state error is about 0.02 cm, and the settling time is approximately 4.5 to 5 seconds.
Sorting Model using Robotic Arm with Image Processing Nguyen-Khoa Tran; Dinh-Khang Nguyen; Phong Luu Nguyen; Nhat-Anh Huynh; Thanh-Hung Tran; Khac-Dinh Nguyen; Xuan-Anh Dinh; Binh-Hau Nguyen; Gia-Phu Nguyen; Minh-Phuoc Cu
Journal of Fuzzy Systems and Control Vol. 4 No. 3 (2026): Vol. 4 No. 3 2026
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i3.387

Abstract

This paper presents the design and implementation of a product sorting model using a robotic arm integrated with image processing techniques. The system consists of a conveyor belt, a vision module, and robotic manipulators that work together to identify and classify objects through a camera and computer vision algorithms that detect product characteristics. The robotic arm then performs the corresponding sorting operation according to product quality requirements. The hardware design includes the construction of the robotic arm, control circuits, and integration with actuators, while the software design focuses on developing image processing algorithms and communication between the vision system and the robot controller. Experimental results show that the system achieves an average size measurement error of approximately ±2 mm, a classification accuracy of about 95%, and an average processing time of 2–3 seconds per product. These results demonstrate reliable recognition and classification performance compared to some previous research models. The proposed model emphasizes the feasibility of combining robotic manipulation and computer vision for automated sorting tasks in industrial applications such as food processing, household tools, and medical instruments, while also serving as a practical training platform for students in technical education. Future improvements may include optimizing vision algorithms, enhancing the mechanical design of the robotic arm, integrating artificial intelligence to improve safety, and expanding the system’s capability to handle more complex classification tasks.
Co-Authors Anh-Tuan Nguyen Bao-Trung Mai Binh-Hau Nguyen Dinh-Khang Nguyen Gia-Phu Nguyen Hai-Duong Nguyen Huynh-The-Hung Nguyen Khac-Dinh Nguyen Minh-Phuoc Cu Minh-Tan Nguyen Nguyen-Khoa Tran Nhat-Anh Huynh Phuc-Khanh Dang Quoc-Bao Nguyen Quoc-Hung Nguyen Quoc-Toan Nguyen Tan-Khang Nguyen Tan-Loc Pham Thanh-Hung Tran That-Ngoc-Hai Ton Truong-Viet Nguyen Van-Hai Nguyen Xuan-Anh Dinh Xuan-Cuong Le