Design of RBF Adaptive Sliding Mode Controller for A Supercavitating Vehicle

Abstract

This paper proposes an adaptive sliding mode control strategy based on RBF (Radial Basis Function) neural network for the supercavitating vehicle system with model uncertainties and external disturbance. Aiming at the unknown items in the model, the control strategy compensates the unknown model uncertainty and external disturbance through the RBF neural network, and derives the neural network weight update strategy according to the Lyapunov stability theory which can guarantee the closed loop system asymptotic stability. The simulation results show that this control strategy can enable the supercavitating vehicle to track reference signal when there are model uncertainties and external disturbance, and ensure the convergence of the trajectory tracking error. Compared with the control input of the sliding mode control strategy without RBF neural network, the control input of the adaptive sliding mode control strategy with RBF neural network is also reduced, which further verifies the effectiveness of the RBF adaptive sliding mode control strategy proposed in this paper.

Keywords

• Adaptive sliding mode control
• RBF neural network
• supercavitating vehicle
• supercavity