Abstract
The autonomous anti-sway control of the ship-mounted crane systems is a tough issue due to the under-actuated characteristic, strong coupling and base excitation. Furthermore, the ship-mounted crane systems are also suffering from unknown dynamics and frictions etc. In some conditions, the ship-mounted cranes may exhibit spherical pendulum effects. These factors make the control problem even more challenging. To solve the above problems, this paper designed a Radical Basis Function Neural Network (RBFNN) based feedback control method for a 5-DOF ship-mounted rotary crane. Specially, the adaptive RBFNN is established to approximate the unknown dynamics online. After that, eschewing any simplification of the dynamic model, a feedback anti-sway control is designed to ensure the cargo could reach to desired position and dampening the payload spherical swing simultaneously. The closed-loop stability is analyzed and the effectiveness of the control method is validated via simulation experiment results.
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This work was supported the National Natural Science Foundation of China under Grant 62103233.
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Li, Z., Chuanjing, H., Liu, C. (2025). Radical Basis Neural Network Based Anti-swing Control for 5-DOF Ship-Mounted Crane. In: Zhang, H., Li, X., Hao, T., Meng, W., Wu, Z., He, Q. (eds) Neural Computing for Advanced Applications. NCAA 2024. Communications in Computer and Information Science, vol 2181. Springer, Singapore. https://doi.org/10.1007/978-981-97-7001-4_2
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DOI: https://doi.org/10.1007/978-981-97-7001-4_2
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