Claim Missing Document
Check
Articles

Found 1 Documents
Search

Intelligent Control for 2D-Crane System Huynh, Trung-Son; Dinh, Dang-Khoa; Tran, Trong-Bang; Dang, Huu-Loc; Le, Dinh-Nguyen-Phuc; Bui, Hung-Thinh; Le, Hoang-Lam; Nguyen, Thanh-Binh; Nguyen, Van-Hiep; Nguyen, Le-Nhat-Minh; Dang, Thien-Quoc; Nguyen, Ngoc-Hung; Nguyen, Thi-Ngoc-Thao; Pham, Huynh-Duc; Nguyen, Xuan-Tien; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.350

Abstract

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.