Kybernetika 59 no. 2, 273-293, 2023

Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode

Yun Chen and Hua ChenDOI: 10.14736/kyb-2023-2-0273

Abstract:

To tackle the underactuated surface vessel (USV) trajectory tracking challenge with input delays and composite disturbances, an integral time-delay sliding mode controller based on backstepping is discussed. First, the law of virtual velocity control is established by coordinate transformation and the position error is caused to converge utilizing the performance function. At the same time, based on the estimation of velocity vector by the high-gain observer (HGO), radial basis function (RBF) neural network is applied to compensate for both the uncertainty of model parameters and external disturbances. The longitudinal and heading control laws are presented in combination with the integral time-delay sliding mode control. Then, on the basis of Lyapunov - Krasovskii functional and stability proof, virtual velocity error is guaranteed to converge to 0 in finite time. Finally, the outcomes of the numerical simulation demonstrate the reliability and efficiency of the proposed approach.

Keywords:

sliding mode, time-delay, backstepping, external disturbances, trajectory tracking, underactuated surface vessels, radial basis function(RBF)

Classification:

93A30, 93Dxx

paper.pdf

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