Geodesic
Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode
Kybernetika, Tome 59 (2023) no. 2, pp. 273-293 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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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.
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.
DOI : 10.14736/kyb-2023-2-0273
Classification : 93A30, 93Dxx
Keywords: underactuated surface vessels; trajectory tracking; time-delay; external disturbances; sliding mode; backstepping; radial basis function(RBF) 
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     title = {Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode},
     journal = {Kybernetika},
     pages = {273--293},
     year = {2023},
     volume = {59},
     number = {2},
     doi = {10.14736/kyb-2023-2-0273},
     mrnumber = {4600378},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2023-2-0273/}
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Chen, Yun; Chen, Hua. Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode. Kybernetika, Tome 59 (2023) no. 2, pp. 273-293. doi: 10.14736/kyb-2023-2-0273

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