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Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators
1
Zhejiang University of Technology, Hangzhou 310023, China
2
Zhejiang University of Science and Technology, Hangzhou 310023, China
3
Department of Information Engineering, University of Padua, 35131 Padova, Italy
* Corresponding Author:
Shuzong Xie,
[email protected]
Volume 1, Issue 2
2024
Received: 17 October 2024
Accepted: 05 December 2024
Published: 18 December 2024
Article Information
Abstract
In engineering applications, high-precision tracking control is crucial for robotic manipulators to successfully complete complex operational tasks. To achieve this goal, this study proposes an adaptive tunable predefined-time backstepping control strategy for uncertain robotic manipulators with external disturbances and model uncertainties. By establishing a novel practical predefined-time stability criterion, a tunable predefined-time backstepping controller is systematically presented, allowing the upper bound of tracking error settling time to be precisely determined by adjusting only one control parameter. To accurately address lumped uncertainty, two updating laws are designed: a fuzzy weight updating law and a boundary adaptive updating law, which together reduce dependence on system model knowledge. In addition, the singularity problem in the predefined-time design process is effectively avoided by constructing the hyperbolic tangent function. The efficacy of the proposed control strategy is verified through numerical simulations.
Graphical Abstract
Keywords
predefined-time control
adaptive fuzzy control
backstepping design
robotic manipulators
Data Availability Statement
Data will be made available on request.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62203384, Grant 62222315 and Grant 62403426; in part by the Zhejiang Provincial Nature Science Foundation of China under Grant MS25F030011 and Grant LZ22F030007.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Shi, H., Xie, S., Chen, Q., Hu, S., & Yi, S. (2024). Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators. ICCK Transactions on Sensing, Communication, and Control, 1(2), 126–135. https://doi.org/10.62762/TSCC.2024.672831
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TY - JOUR AU - Shi, Huihui AU - Xie, Shuzong AU - Chen, Qiang AU - Hu, Shuangyi AU - Yi, Shenglun PY - 2024 DA - 2024/12/18 TI - Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators JO - ICCK Transactions on Sensing, Communication, and Control T2 - ICCK Transactions on Sensing, Communication, and Control JF - ICCK Transactions on Sensing, Communication, and Control VL - 1 IS - 2 SP - 126 EP - 135 DO - 10.62762/TSCC.2024.672831 UR - https://www.icck.org/article/abs/TSCC.2024.672831 KW - predefined-time control KW - adaptive fuzzy control KW - backstepping design KW - robotic manipulators AB - In engineering applications, high-precision tracking control is crucial for robotic manipulators to successfully complete complex operational tasks. To achieve this goal, this study proposes an adaptive tunable predefined-time backstepping control strategy for uncertain robotic manipulators with external disturbances and model uncertainties. By establishing a novel practical predefined-time stability criterion, a tunable predefined-time backstepping controller is systematically presented, allowing the upper bound of tracking error settling time to be precisely determined by adjusting only one control parameter. To accurately address lumped uncertainty, two updating laws are designed: a fuzzy weight updating law and a boundary adaptive updating law, which together reduce dependence on system model knowledge. In addition, the singularity problem in the predefined-time design process is effectively avoided by constructing the hyperbolic tangent function. The efficacy of the proposed control strategy is verified through numerical simulations. SN - 3068-9287 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Shi2024Adaptive,
author = {Huihui Shi and Shuzong Xie and Qiang Chen and Shuangyi Hu and Shenglun Yi},
title = {Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators},
journal = {ICCK Transactions on Sensing, Communication, and Control},
year = {2024},
volume = {1},
number = {2},
pages = {126-135},
doi = {10.62762/TSCC.2024.672831},
url = {https://www.icck.org/article/abs/TSCC.2024.672831},
abstract = {In engineering applications, high-precision tracking control is crucial for robotic manipulators to successfully complete complex operational tasks. To achieve this goal, this study proposes an adaptive tunable predefined-time backstepping control strategy for uncertain robotic manipulators with external disturbances and model uncertainties. By establishing a novel practical predefined-time stability criterion, a tunable predefined-time backstepping controller is systematically presented, allowing the upper bound of tracking error settling time to be precisely determined by adjusting only one control parameter. To accurately address lumped uncertainty, two updating laws are designed: a fuzzy weight updating law and a boundary adaptive updating law, which together reduce dependence on system model knowledge. In addition, the singularity problem in the predefined-time design process is effectively avoided by constructing the hyperbolic tangent function. The efficacy of the proposed control strategy is verified through numerical simulations.},
keywords = {predefined-time control, adaptive fuzzy control, backstepping design, robotic manipulators},
issn = {3068-9287},
publisher = {Institute of Central Computation and Knowledge}
}
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