Neural network based dynamic surface second order sliding mode control for AUVs

Kai Zhang; Tieshan Li; Zifu Li; C. L. Philip Chen

doi:10.1007/978-981-10-5230-9_41

Neural network based dynamic surface second order sliding mode control for AUVs

Kai Zhang
, Tieshan Li^*
, Zifu Li
, C. L. Philip Chen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, a novel neural network based dynamic surface second order sliding mode control algorithm is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicles (AUVs) with modeling errors under external disturbances. The controller designed is capable of strengthening robustness of the system and attenuates inherent chattering of classical sliding mode control effectively. An innovative neural network compensator is designed to counteract effects of modeling errors, furthermore, the norm of the ideal weighting vector in neural network system is regarded as the estimated parameter, such that there is only one parameter needs to be adjusted. Meanwhile, the effect of external disturbances is handled by means of hyperbolic tangent function. As a result, the Lyapunov based stability analysis is provided to guarantee semi-global uniform boundedness of all closed-loop signals. Verification of the effectiveness of the proposed algorithm is done through simulation results.

Original language	English
Title of host publication	Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers
Editors	Fuchun Sun, Huaping Liu, Dewen Hu
Publisher	Springer Verlag
Pages	417-424
Number of pages	8
ISBN (Print)	9789811052293
DOIs	https://doi.org/10.1007/978-981-10-5230-9_41
State	Published - 2017
Externally published	Yes
Event	3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016 - Beijing, China Duration: 19 Nov 2016 → 23 Nov 2016

Publication series

Name	Communications in Computer and Information Science
Volume	710
ISSN (Print)	1865-0929

Conference

Conference	3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016
Country/Territory	China
City	Beijing
Period	19/11/16 → 23/11/16

Keywords

Autonomous underwater vehicle (AUV)
Dynamic surface control (DSC)
Hyperbolic tangent function
Neural network (NN)
Second order sliding mode control
Trajectory tracking

Access to Document

10.1007/978-981-10-5230-9_41

Cite this

Zhang, K., Li, T., Li, Z., & Philip Chen, C. L. (2017). Neural network based dynamic surface second order sliding mode control for AUVs. In F. Sun, H. Liu, & D. Hu (Eds.), Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers (pp. 417-424). (Communications in Computer and Information Science; Vol. 710). Springer Verlag. https://doi.org/10.1007/978-981-10-5230-9_41

Zhang, Kai ; Li, Tieshan ; Li, Zifu et al. / Neural network based dynamic surface second order sliding mode control for AUVs. Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers. editor / Fuchun Sun ; Huaping Liu ; Dewen Hu. Springer Verlag, 2017. pp. 417-424 (Communications in Computer and Information Science).

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title = "Neural network based dynamic surface second order sliding mode control for AUVs",

abstract = "In this paper, a novel neural network based dynamic surface second order sliding mode control algorithm is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicles (AUVs) with modeling errors under external disturbances. The controller designed is capable of strengthening robustness of the system and attenuates inherent chattering of classical sliding mode control effectively. An innovative neural network compensator is designed to counteract effects of modeling errors, furthermore, the norm of the ideal weighting vector in neural network system is regarded as the estimated parameter, such that there is only one parameter needs to be adjusted. Meanwhile, the effect of external disturbances is handled by means of hyperbolic tangent function. As a result, the Lyapunov based stability analysis is provided to guarantee semi-global uniform boundedness of all closed-loop signals. Verification of the effectiveness of the proposed algorithm is done through simulation results.",

keywords = "Autonomous underwater vehicle (AUV), Dynamic surface control (DSC), Hyperbolic tangent function, Neural network (NN), Second order sliding mode control, Trajectory tracking",

author = "Kai Zhang and Tieshan Li and Zifu Li and \{Philip Chen\}, \{C. L.\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016 ; Conference date: 19-11-2016 Through 23-11-2016",

year = "2017",

doi = "10.1007/978-981-10-5230-9\_41",

language = "英语",

isbn = "9789811052293",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

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Zhang, K, Li, T, Li, Z & Philip Chen, CL 2017, Neural network based dynamic surface second order sliding mode control for AUVs. in F Sun, H Liu & D Hu (eds), Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers. Communications in Computer and Information Science, vol. 710, Springer Verlag, pp. 417-424, 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016, Beijing, China, 19/11/16. https://doi.org/10.1007/978-981-10-5230-9_41

Neural network based dynamic surface second order sliding mode control for AUVs. / Zhang, Kai; Li, Tieshan; Li, Zifu et al.
Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers. ed. / Fuchun Sun; Huaping Liu; Dewen Hu. Springer Verlag, 2017. p. 417-424 (Communications in Computer and Information Science; Vol. 710).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Neural network based dynamic surface second order sliding mode control for AUVs

AU - Zhang, Kai

AU - Li, Tieshan

AU - Li, Zifu

AU - Philip Chen, C. L.

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017.

PY - 2017

Y1 - 2017

N2 - In this paper, a novel neural network based dynamic surface second order sliding mode control algorithm is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicles (AUVs) with modeling errors under external disturbances. The controller designed is capable of strengthening robustness of the system and attenuates inherent chattering of classical sliding mode control effectively. An innovative neural network compensator is designed to counteract effects of modeling errors, furthermore, the norm of the ideal weighting vector in neural network system is regarded as the estimated parameter, such that there is only one parameter needs to be adjusted. Meanwhile, the effect of external disturbances is handled by means of hyperbolic tangent function. As a result, the Lyapunov based stability analysis is provided to guarantee semi-global uniform boundedness of all closed-loop signals. Verification of the effectiveness of the proposed algorithm is done through simulation results.

AB - In this paper, a novel neural network based dynamic surface second order sliding mode control algorithm is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicles (AUVs) with modeling errors under external disturbances. The controller designed is capable of strengthening robustness of the system and attenuates inherent chattering of classical sliding mode control effectively. An innovative neural network compensator is designed to counteract effects of modeling errors, furthermore, the norm of the ideal weighting vector in neural network system is regarded as the estimated parameter, such that there is only one parameter needs to be adjusted. Meanwhile, the effect of external disturbances is handled by means of hyperbolic tangent function. As a result, the Lyapunov based stability analysis is provided to guarantee semi-global uniform boundedness of all closed-loop signals. Verification of the effectiveness of the proposed algorithm is done through simulation results.

KW - Autonomous underwater vehicle (AUV)

KW - Dynamic surface control (DSC)

KW - Hyperbolic tangent function

KW - Neural network (NN)

KW - Second order sliding mode control

KW - Trajectory tracking

UR - https://www.scopus.com/pages/publications/85026740329

U2 - 10.1007/978-981-10-5230-9_41

DO - 10.1007/978-981-10-5230-9_41

M3 - 会议稿件

AN - SCOPUS:85026740329

SN - 9789811052293

T3 - Communications in Computer and Information Science

SP - 417

EP - 424

BT - Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers

A2 - Sun, Fuchun

A2 - Liu, Huaping

A2 - Hu, Dewen

PB - Springer Verlag

T2 - 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016

Y2 - 19 November 2016 through 23 November 2016

ER -

Zhang K, Li T, Li Z, Philip Chen CL. Neural network based dynamic surface second order sliding mode control for AUVs. In Sun F, Liu H, Hu D, editors, Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers. Springer Verlag. 2017. p. 417-424. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-5230-9_41

Neural network based dynamic surface second order sliding mode control for AUVs

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