Command filtered-based neuro-adaptive robust finite-time trajectory tracking control of autonomous underwater vehicles under stochastic perturbations

Abstract

In this paper, the problem of finite-time trajectory tracking control is studied and addressed for a 6 degree of freedom (DOF) autonomous underwater vehicle (AUV) subjected to unknown dynamic model, stochastic perturbations, external disturbances (matched and mismatched) and saturation input nonlinearities. Based on the backstepping control approach, novel finite-time control inputs are designed and proposed. Artificial neural networks (ANNs) and finite-time adaptation laws are exploited to approximate the nonlinear dynamics of AUV, the stochastic perturbations and the upper bound of external disturbances. To handle the destructive effects of saturation input nonlinearities, finite-time auxiliary system method is utilized. To overcome the explosion of complexity problem of backstepping control strategy, compensator-based finite-time command filter approach is exploited. By utilizing the Lyapunov stability theorem, it is mathematically proven and demonstrated that the suggested nonlinear control inputs are able to guarantee the semi-global finite-time stability in probability (SGFSP) of the closed-loop AUV system. Finally, numerical simulations are carried out to illustrate and depict the effectiveness and performance of the proposed neuro-adaptive robust finite-time control scheme.