Design of ROV Adaptive Sliding Mode Control System for Underwater Vehicle Based on RBF Neural Network

Wei Chen, S. Hu, Qingyu Wei
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Abstract

The dynamic positioning control of ROV near the water surface under wave disturbance is still a challenging problem. The principle of sliding mode control and the method of approximating unknown function by RBF neural network are studied. The adaptive sliding mode controller of RBF neural network is designed. The stability and convergence of the proposed algorithm are deduced and verified, and compared with the simulation results of traditional adaptive sliding mode control methods. The simulation results show that the ROV's trajectory tracking effect is good in the wave disturbance environment. The experimental results prove the effectiveness of the method and achieved satisfactory performance.
基于RBF神经网络的水下机器人自适应滑模控制系统设计
波浪扰动下近水面ROV的动态定位控制仍然是一个具有挑战性的问题。研究了滑模控制原理和RBF神经网络逼近未知函数的方法。设计了RBF神经网络自适应滑模控制器。推导并验证了该算法的稳定性和收敛性,并与传统自适应滑模控制方法的仿真结果进行了比较。仿真结果表明,在波浪扰动环境下,ROV的轨迹跟踪效果良好。实验结果证明了该方法的有效性,取得了令人满意的效果。
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