Research on Attitude Stability Control Method and Experiment of Underwater Vehicle under Water Flow Interference

The paper investigates an attitude control of underwater vehicle in the underwater operational condition. In general, the underwater vehicle is affected by nonlinear factors such as water flow and operation interference, which will lead to the change of the attitude of the underwater vehicle, and then affect the operation accuracy. Focus on this problem, this paper proposes an adaptive sliding mode attitude control method based on the Radial Basis Function (RBF) neural network. In the aspect of design, the attitude control structure based on the change of the gravity center is used to adjust the attitude of underwater vehicle. Then, the RBF neural network is used to identify the uncertainties online, and for the underwater vehicle system with large inertia, a $\mathrm{K}_{\mathrm{p}}$ control link based on sliding mode switch function is proposed to reduce the time of attitude adjustment. Through Lyapunov stability analysis, it is verified that the attitude control error and its rate of change can quickly converge to zero and stably. Finally, the analysis of experimental data shows the effectiveness of the proposed method.