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Nguyen, Anh-Huy
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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.
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.
Co-Authors Le, Hoang-Lam Le, Hoang-Linh Le, Thi-Hong-Lam Nguyen, Binh-Hau Nguyen, Duc-Anh-Quan Nguyen, Hai-Thanh Nguyen, Minh-Tam Nguyen, Ngoc-Hung Nguyen, Phong-Luu Nguyen, Phuong-Quang Nguyen, Quang-Thien Nguyen, Thanh-Binh Nguyen, Thi-Ngoc-Thao Nguyen, Van-Dong-Hai Nguyen, Van-Hiep Phung, Son-Thanh Vu, Dinh-Minh