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 (Peneliti Teknologi Teknik Indonesia, 2026)
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.