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

Yun Chen; Hua Chen

Kybernetika (2023)

  • Volume: 59, Issue: 2, page 273-293
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Chen, Yun, and Chen, Hua. "Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode." Kybernetika 59.2 (2023): 273-293. <http://eudml.org/doc/299078>.

@article{Chen2023,
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.},
author = {Chen, Yun, Chen, Hua},
journal = {Kybernetika},
keywords = {underactuated surface vessels; trajectory tracking; time-delay; external disturbances; sliding mode; backstepping; radial basis function(RBF)},
language = {eng},
number = {2},
pages = {273-293},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode},
url = {http://eudml.org/doc/299078},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Chen, Yun
AU - Chen, Hua
TI - Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 2
SP - 273
EP - 293
AB - 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.
LA - eng
KW - underactuated surface vessels; trajectory tracking; time-delay; external disturbances; sliding mode; backstepping; radial basis function(RBF)
UR - http://eudml.org/doc/299078
ER -

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