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All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Quang-Thien Nguyen
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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Development of an AI and Webserver-integrated Smart Automated Storage and Retrieval System Quang-Thien Nguyen; Thien-Bao Truong; Tan-Huy Tran; Tan-Loc Nguyen; Ngoc-Son Vo; Nguyen-Khang Bui; Van-Dong-Hai Nguyen; Thanh-An Cao; Thi-Ngoc-Thao Nguyen; Thi-Hong-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.v4i2.381

Abstract

In recent years, Automated Storage and Retrieval Systems (AS/RS) and their development have been a notable trend of modern warehouse management by automating the sequential and precise processes of storing, sorting, and retrieving goods. Driven by the convergence of mechatronic systems, Industrial Internet of Things (IIoT), Artificial Intelligence (AI), cloud storage, and edge-based management systems, the potential and practical benefits of AS/RS can be significantly amplified when effectively combined with these trends. In this field, although some works are presented, they often lack specialization for the Vietnamese industrial environment and sustainability. Therefore, this research presents the development of an intelligent AS/RS, incorporating AI-based label processing and webserver-based control to enhance warehouse management efficiency. Experimental evaluations demonstrate that the system achieves high reliability in product classification and storage tasks, providing a scalable solution for modern smart logistics with real-time data synchronization capabilities via a Node-RED web server.
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.
Real-Time Trajectory Tracking Control of a DC Motor Using a Self-Tuning Regulator with Online Parameter Estimation Quang-Thien Nguyen; Hoang-Linh Le; Anh-Huy Nguyen; Duc-Anh-Quan Nguyen; Van-Dong-Hai Nguyen; Minh-Tam Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Phuong-Quang Nguyen; Thi-Hong-Lam Le; Binh-Hau Nguyen; Dinh-Minh Vu
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 Anh-Huy Nguyen Binh-Hau Nguyen Dinh-Minh Vu Duc-Anh-Quan Nguyen Hai-Thanh Nguyen Hoang-Lam Le Hoang-Linh Le Minh-Tam Nguyen Ngoc-Hung Nguyen Ngoc-Son Vo Nguyen-Khang Bui Phong-Luu Nguyen Phuong-Quang Nguyen Son-Thanh Phung Tan-Huy Tran Tan-Loc Nguyen Thanh-An Cao Thanh-Binh Nguyen Thi-Hong-Lam Le Thi-Ngoc-Thao Nguyen Thien-Bao Truong Van-Dong-Hai Nguyen Van-Hiep Nguyen