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

Voir la notice de l'article

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) 
@article{10_14736_kyb_2023_2_0273,
     author = {Chen, Yun and Chen, Hua},
     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/}
}
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
SP  - 273
EP  - 293
VL  - 59
IS  - 2
UR  - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2023-2-0273/
DO  - 10.14736/kyb-2023-2-0273
LA  - en
ID  - 10_14736_kyb_2023_2_0273
ER  - 
%0 Journal Article
%A Chen, Yun
%A Chen, Hua
%T Prescribed performance control of underactuated surface vessels' trajectory using a neural network and integral time-delay sliding mode
%J Kybernetika
%D 2023
%P 273-293
%V 59
%N 2
%U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2023-2-0273/
%R 10.14736/kyb-2023-2-0273
%G en
%F 10_14736_kyb_2023_2_0273
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

[1] Avila, J. P. J., Donha, D. C., Amowski, J. C. Ad: Experimental model identification of open-frame underwater vehicles. Ocean Engrg. 60 (2013), 81-94. | DOI

[2] Behtash, S.: Robust output tracking for non-linear systems. Int. J. Control 51 (1990), 6, 1381-1407. | DOI | MR

[3] Chen, H., Chen, Y., Wang, M.: Trajectory tracking for underactuated surface vessels with time delays and unknown control directions. IET Control Theory Appl. 16 (2022), 6, 587-599. | DOI

[4] Chen, W., Wei, Y., Zeng, J., Hu, J., Wang, Z.: Adaptive backstepping control of underactuated AUV based on disturbance observer. J. Central South University 48 (2017), 1, 69-76.

[5] Chu, Z., Zhu, D., Yang, S. X., E., G., Jan: Adaptive Sliding mode control for depth trajectory tracking of remotely operated vehicle with thruster nonlinearity. J. Navigation 70 (2017), 1, 149-164. | DOI

[6] Druzhinina, O., Sedova, N.: Optimization Problems in tracking control design for an underactuated ship with feedback delay, state and control constraints. Optim. Appl. 12422 (2020), 71-85. | DOI | MR

[7] Du, J., Li, J.: Finite-time prescribed performance control for the three-dimension trajectory tracking of underactuated autonomous underwater vehicles. Control Theory Appl. 39 (2022), 383-392. | DOI

[8] Feng, Z., Lam, J., Yang, G.-H.: Optimal partitioning method for stability analysis of continuous/discrete delay systems. Int. J. Robust Nonlinear Control 25 (2015), 4, 559-574. | DOI | MR

[9] Jia, Z., Hu, Z., Zhang, W.: Adaptive output-feedback control with prescribed performance for trajectory tracking of underactuated surface vessels. ISA Trans. 95 (2019), 18-56. | DOI

[10] Jian, X., Man, W., Lei, Q.: Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles. Ocean Engrg. 105 (2015), 54-63. | DOI

[11] Lakhekar, G. V., Waghmare, L. M.: Adaptive fuzzy exponential terminal sliding mode controller design for nonlinear trajectory tracking control of autonomous underwater vehicle. Int. J. Dynamics Control 6.4 (2018), 1690-1705. | DOI | MR

[12] Liao, Z. Y., Dai, Y. S., Li, L. G., Jin, J. C., F., Shao: Overview of unmanned surface vehicle motion control methods. Marine Sci. 44 (2020), 3, 153-162.

[13] Liao, Y. L., Zhang, M. J., Wan, L., Li, Y.: Trajectory tracking control for underactuated unmanned surface vehicles with dynamic uncertainties. J. Central South Univ. 23 (2016), 2, 370-378. | DOI

[14] Liu, Z.: Practical backstepping control for underactuated ship path following associated with disturbances. IET Intell. Transport Systems 13 (2018), 5, 834-840. | DOI

[15] Manley, J. E.: Unmanned surface vehicles, 15 years of development. Oceans (2008), Supplement, 1-4.

[16] Marco, B., Massimo, C., Lionel, L.: Path-following algorithms and experiments for an autonomous surface vehicle. IFAC Proc. Vol. 40 (2007), 17, 81-86. | DOI

[17] Min, Y., Liu, Y.: Barbalat Lemma and its application in analysis of system stability. J. Shandong Univ., Engrg. Sci. (2007), 51-55+114. | DOI

[18] Pastore, T., Djapic, V.: Improving autonomy and control of autonomous surface vehicles in port protection and mine countermeasure scenarios. J. Field Robotics 27 (2010), 6, 903-914. | DOI

[19] Qijia, Y.: Robust fixed-time trajectory tracking control of marine surface vessel with feedforward disturbance compensation. Int. J. Systems Sci. 53 (2022), 4, 726-742. | DOI | MR

[20] Qiu, B., Wang, G., Fan, Y., Mu, D., Sun, X.: Adaptive sliding mode trajectory tracking control for unmanned surface vehicle with modeling uncertainties and input saturation. Appl. Sci. 9 (2019), 6, 1240. | DOI

[21] Qudrat, K., Rini, A.: Neuro-adaptive dynamic integral sliding mode control design with output differentiation observer for uncertain higher order MIMO nonlinear systems. Neurocomputing 226 (2017), 126-134. | DOI

[22] Ramakrishnan, K., Ray, G.: Delay-range-dependent stability criterion for interval time-delay systems with nonlinear perturbations. International Journal of Automation and Computing, vol.8.1, (2011), 141-146. | DOI | MR

[23] Wang, F., Chao, Z., Huang, L.: Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode. Cluster Comput. 22 (2019), 3, 5799-5809. | DOI

[24] Xu, D., Liu, Z., Zhou, X., Yang, L., Huang, L.: Trajectory tracking of underactuated unmanned surface vessels: non-singular terminal sliding control with nonlinear disturbance observer. Appl. Sci. 12 (2022), 6, 3004. | DOI

[25] Yu, L., Guoqing, Z., Lei, Q., Weidong, Z.: Adaptive output-feedback formation control for underactuated surface vessels. Int. J. Control 93 (2020), 3, 400-409. | DOI | MR

[26] Zhou, J., Xinyi, Z., Zhiguang, F., Di, W.: Trajectory tracking sliding mode control for underactuated autonomous underwater vehicles with time delays. Int. J. Advanced Robotic Systems 17 (2020), 3, 1729881420916276. | DOI

[27] Zhou, J., Zhao, X., Chen, T., Yan, Z., Yang, Z.: Trajectory tracking control of an underactuated AUV based on backstepping sliding mode with state prediction. IEEE Access 7 (2019), 181983-181993. | DOI

[28] Zou, L., Liu, H., Tian, X.: Robust neural network trajectory-tracking control of underactuated surface vehicles considering uncertainties and unmeasurable velocities. IEEE Access9 (2021), 117629-117638. | DOI

Cité par Sources :