Abstract
Both trajectory tracking (TT) and fin roll reduction (FRR) are fundamental marine applications, and they are usually studied separately in previous studies. Actually, the roll motion often occurs during the trajectory tracking in waves; therefore, they should be studied together. In this work, we consider the trajectory tracking and fin roll reduction of marine vessel as an integral system. It includes three system inputs, namely, the force in surge, the control moment in roll, and the control torque in yaw, while four degrees of freedom (DoF), i.e., position, roll angle and yaw angle are needed to be controlled. Through combining the hierarchical sliding mode approach and neural network technique, a novel control algorithm is proposed. The neural network is introduced to deal with model uncertainty. Lyapunov stability theorem ensures stability of the close-loop system, and various simulations are provided to validate the effectiveness and performance of the proposed algorithm.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (Nos. 51179019, 61374114), the Fundamental Research Program for Key Laboratory of the Education Department of Liaoning Province (LZ2015006), the Natural Science Foundation of Liaoning (20170580081), and the Fundamental Research Funds for the Central Universities under Grants 3132016313, 3132017114 and 3132016311, Postdoctoral innovation talent support plan (BX201700041).
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Liu, C., Li, J., Zhao, R., Li, T. (2018). NN - Sliding Mode Control Design for Trajectory Tracking and Roll Reduction of Marine Vessels. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_76
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