Abstract
Aiming at the path tracking problem of AUV (autonomous underwater vehicle) in the process of docking, a fuzzy sliding mode control algorithm based on RBF (radial basis function) neural network is proposed. Firstly, the sliding mode control is used to track the trajectory, the fuzzy control is used to continuously correct the parameters of the exponential reaching rate in the sliding mode control to alleviate the shaking problem. Then the RBF neural network is used to compensate uncertainty in the AUV motion model and the external unknown interference. Finally, the stability of the control system is proved by Lyapunov stability theory. The simulation result shows that the designed control algorithm can track the trajectory of AUV effectively. By comparing tracking effects with traditional sliding mode control and fuzzy sliding mode control, it is proved that the proposed control method has faster tracking speed, better stability and better tracking performance.
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Acknowledgements
This work was supported by Jiangsu International Science and Technology Cooperation Project (No. BZ2016031), and Jiangsu University of Science and Technology Ph.D. Fund Project, which are greatly appreciated your review.
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Wang, X., Zhang, Y., Xue, Z. (2019). Fuzzy Sliding Mode Control Based on RBF Neural Network for AUV Path Tracking. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11741. Springer, Cham. https://doi.org/10.1007/978-3-030-27532-7_56
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DOI: https://doi.org/10.1007/978-3-030-27532-7_56
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