The cascade backstepping controller design for supercavitating vehicle based on RBFNN observer

Aiming at the model uncertainty in design process of longitudinal motion control system of supercavitating vehicle, a backstepping controller is designed by combining with Radial Basis Function neural network (RBFNN). Considering that the environment of the supercavitating vehicle is complex during its traversal, it is not possible to measure all states directly. The calculation of planing force with nonlinear characteristics is related to vertical velocity, therefore, the accuracy of vertical velocity has impact on the value of planing force directly. A state observer is designed to estimate vertical velocity. In this paper, based on the backstepping method, the uncertain part of matrix coefficient in cascade control model of supercavitating vehicle is approximated by RBFNN. The output of the observer approximates the state variables, then, a depth tracking control law of the vehicle is obtained according to the observed values and the weight of neural network is calculated by Lyapunov function. Finally, it is proved that the control law can ensure the uniform ultimate boundedness of the closed-loop system.

Automated summary

In this paper, a backstepping controller is designed for the longitudinal motion control system of a supercavitating vehicle by combining with Radial Basis Function neural network (RBFNN) to address the issue of model uncertainty. Due to the complexity of the environment during traversal, it is not feasible to directly measure all states. A state observer is designed to estimate the vertical velocity, which plays a crucial role in the calculation of planing force with nonlinear characteristics. The uncertain part of the matrix coefficient in the cascade control model is approximated by RBFNN, and a depth tracking control law is obtained based on the observed values and the weight of the neural network calculated by Lyapunov function. The control law is proven to ensure the uniform ultimate boundedness of the closed-loop system.