Modeling, Position Tracking and Anti-Sway Control of a 3D Overhead Crane Using Intelligent Control Strategies

Resumen

This study presents the modeling and intelligent control of a 3D industrial crane, an academic prototype that closely replicates the dynamics of real-world gantry cranes used in industrial and port operations. The main objective is to minimize load sway and achieve precise positioning along the three spatial axes, thereby enhancing safety and operational efficiency. The theoretical framework is based on advanced intelligent control techniques, specifically Fuzzy Logic Control (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS), which combine heuristic knowledge and adaptive learning to address the nonlinear, multi-input multi-output nature of the crane system. The crane dynamics were modeled using Newtonian mechanics, with system equations implemented in MATLAB/Simulink. Separate controllers for each axis were designed, tested, and compared through simulations. The results show that FLC delivers effective disturbance rejection and stable recovery, while ANFIS achieves faster transient responses and superior vertical-axis performance, albeit with limitations in horizontal motion control due to training data quality. Robustness tests confirmed the superior disturbance tolerance of FLC.