Abstract
To reduce the roll of a surface ship, a robust fin controller based on functional-link neural networks is proposed. The plant consists of the ship roll dynamics and that of the fin actuators. Modeling errors and the environmental disturbance induced by waves are considered in the cascaded roll system, which are identified by the neural networks. Lyapunov function is employed in the controller design, which guarantees the stability of the fin stabilizer. Numerical simulation demonstrates the good performance of the roll reduction based on the controller proposed.
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Luo, W., Lv, W., Zou, Z. (2013). Robust Fin Control for Ship Roll Stabilization by Using Functional-Link Neural Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_28
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DOI: https://doi.org/10.1007/978-3-642-39068-5_28
Publisher Name: Springer, Berlin, Heidelberg
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