Adaptive model-parameter-free nonsingular fixed-time sliding mode control for underwater cleaning vehicle

Abstract

Considering system uncertainties, cavitation water, tether force and other unknown external disturbances, this paper proposes a model-parameter-free nonsingular fixed-time sliding mode control (SMC) for trajectory tracking of underwater cleaning vehicle. The vehicle's dynamical model is transformed into Euler-Lagrange equation in this paper, and a local recurrent neural network (RNN) is employed to estimate the model uncertainties. Based on local RNN, an adaptive nonsingular fixed-time SMC is derived by Lyapunov theory to guarantee the trajectory tracking error of vehicle converge to a minor filed within fixed-time. The effectiveness of the proposed control scheme and its superiority to finite-time SMC are substantiated with the simulation results.