Intelligent Control for 2D-Crane System
Keywords:
2D Crane System, Fuzzy Logic Control, Genetic Algorithm, Neural Network, Adaptive Neuro-Fuzzy Inference SystemAbstract
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.
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Copyright (c) 2026 Trung-Son Huynh, Dang-Khoa Dinh, Trong-Bang Tran, Huu-Loc Dang, Dinh-Nguyen-Phuc Le, Hung-Thinh Bui, Hoang-Lam Le, Thanh-Binh Nguyen, Van-Hiep Nguyen, Le-Nhat-Minh Nguyen, Thien-Quoc Dang, Ngoc-Hung Nguyen, Thi-Ngoc-Thao Nguyen, Huynh-Duc Pham, Xuan-Tien Nguyen, Van-Dong-Hai Nguyen
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