Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems

Wenhao Si; Wei Liu; Shichang Guo; Xianglong Meng; Weiguang Tong; Zhuang Liu; Yue Zhao; Jianxing Liu

doi:10.1109/ICUS66297.2025.11294126

Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems

Wenhao Si
, Wei Liu
, Shichang Guo
, Xianglong Meng
, Weiguang Tong
, Zhuang Liu
, Yue Zhao^*
, Jianxing Liu

^*Corresponding author for this work

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

Abstract

This paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme integrated with adaptive Radial Basis Function Neural Network (RBFNN) for trajectory tracking of uncertain robotic systems. First, a comprehensive dynamic model incorporating external disturbances and parametric uncertainties is established. Subsequently, an NFTSMC-based controller integrated with adaptive RBFNN is designed to guarantee high-precision trajectory tracking with rapid convergence. Rigorous simulations on a planar two-link Free-Floating Space Robot (FFSR) demonstrate the proposed method's superior effectiveness, robustness, and disturbance rejection capabilities compared to conventional approaches.

Original language	English
Title of host publication	Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
Editors	Rong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1493-1498
Number of pages	6
ISBN (Electronic)	9798331526726
DOIs	https://doi.org/10.1109/ICUS66297.2025.11294126
State	Published - 2025
Externally published	Yes
Event	2025 IEEE International Conference on Unmanned Systems, ICUS 2025 - Changzhou, China Duration: 18 Sep 2025 → 19 Sep 2025

Publication series

Name	Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

Conference

Conference	2025 IEEE International Conference on Unmanned Systems, ICUS 2025
Country/Territory	China
City	Changzhou
Period	18/09/25 → 19/09/25

Keywords

nonsingular fast terminal sliding mode control
radial basis function neural network
trajectory tracking
uncertain robotic systems

Access to Document

10.1109/ICUS66297.2025.11294126

Cite this

Si, W., Liu, W., Guo, S., Meng, X., Tong, W., Liu, Z., Zhao, Y., & Liu, J. (2025). Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems. In R. Song (Ed.), Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 (pp. 1493-1498). (Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS66297.2025.11294126

Si, Wenhao ; Liu, Wei ; Guo, Shichang et al. / Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems. Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. editor / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 1493-1498 (Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025).

@inproceedings{b7fe1f33dc81430583f4f7d0182c2d1e,

title = "Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems",

abstract = "This paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme integrated with adaptive Radial Basis Function Neural Network (RBFNN) for trajectory tracking of uncertain robotic systems. First, a comprehensive dynamic model incorporating external disturbances and parametric uncertainties is established. Subsequently, an NFTSMC-based controller integrated with adaptive RBFNN is designed to guarantee high-precision trajectory tracking with rapid convergence. Rigorous simulations on a planar two-link Free-Floating Space Robot (FFSR) demonstrate the proposed method's superior effectiveness, robustness, and disturbance rejection capabilities compared to conventional approaches.",

keywords = "nonsingular fast terminal sliding mode control, radial basis function neural network, trajectory tracking, uncertain robotic systems",

author = "Wenhao Si and Wei Liu and Shichang Guo and Xianglong Meng and Weiguang Tong and Zhuang Liu and Yue Zhao and Jianxing Liu",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 ; Conference date: 18-09-2025 Through 19-09-2025",

year = "2025",

doi = "10.1109/ICUS66297.2025.11294126",

language = "英语",

series = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1493--1498",

editor = "Rong Song",

booktitle = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",

address = "美国",

}

Si, W, Liu, W, Guo, S, Meng, X, Tong, W, Liu, Z , Zhao, Y & Liu, J 2025, Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems. in R Song (ed.), Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025, Institute of Electrical and Electronics Engineers Inc., pp. 1493-1498, 2025 IEEE International Conference on Unmanned Systems, ICUS 2025, Changzhou, China, 18/09/25. https://doi.org/10.1109/ICUS66297.2025.11294126

Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems. / Si, Wenhao; Liu, Wei; Guo, Shichang et al.
Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. ed. / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2025. p. 1493-1498 (Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025).

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

TY - GEN

T1 - Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems

AU - Si, Wenhao

AU - Liu, Wei

AU - Guo, Shichang

AU - Meng, Xianglong

AU - Tong, Weiguang

AU - Liu, Zhuang

AU - Zhao, Yue

AU - Liu, Jianxing

PY - 2025

Y1 - 2025

N2 - This paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme integrated with adaptive Radial Basis Function Neural Network (RBFNN) for trajectory tracking of uncertain robotic systems. First, a comprehensive dynamic model incorporating external disturbances and parametric uncertainties is established. Subsequently, an NFTSMC-based controller integrated with adaptive RBFNN is designed to guarantee high-precision trajectory tracking with rapid convergence. Rigorous simulations on a planar two-link Free-Floating Space Robot (FFSR) demonstrate the proposed method's superior effectiveness, robustness, and disturbance rejection capabilities compared to conventional approaches.

AB - This paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme integrated with adaptive Radial Basis Function Neural Network (RBFNN) for trajectory tracking of uncertain robotic systems. First, a comprehensive dynamic model incorporating external disturbances and parametric uncertainties is established. Subsequently, an NFTSMC-based controller integrated with adaptive RBFNN is designed to guarantee high-precision trajectory tracking with rapid convergence. Rigorous simulations on a planar two-link Free-Floating Space Robot (FFSR) demonstrate the proposed method's superior effectiveness, robustness, and disturbance rejection capabilities compared to conventional approaches.

KW - nonsingular fast terminal sliding mode control

KW - radial basis function neural network

KW - trajectory tracking

KW - uncertain robotic systems

UR - https://www.scopus.com/pages/publications/105031874087

U2 - 10.1109/ICUS66297.2025.11294126

DO - 10.1109/ICUS66297.2025.11294126

M3 - 会议稿件

AN - SCOPUS:105031874087

T3 - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

SP - 1493

EP - 1498

BT - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

A2 - Song, Rong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

Y2 - 18 September 2025 through 19 September 2025

ER -

Si W, Liu W, Guo S, Meng X, Tong W, Liu Z et al. Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems. In Song R, editor, Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 1493-1498. (Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025). doi: 10.1109/ICUS66297.2025.11294126

Adaptive Neural Network-Based Fast Sliding Mode Trajectory Tracking Control for Uncertain Robotic Systems

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