-
CiteScore
-
Impact Factor
IECE Transactions on Sensing, Communication, and Control, 2024, Volume 1, Issue 2: 126-135

Free Access | Research Article | 18 December 2024
1 Zhejiang University of Technology, Hangzhou 310023, China
2 Zhejiang University of Science and Technology, Hangzhou 310023, China
3 University of Padova, 35131 Padova, Italy
* Corresponding author: Shuzong Xie, email: [email protected]
Received: 17 October 2024, Accepted: 05 December 2024, Published: 18 December 2024  

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
Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators

Keywords
predefined-time control
adaptive fuzzy control
backstepping design
robotic manipulators

References

[1] Yang, J., Wang, Y., Wang, T., Hu, Z., Yang, X., & Rodriguez-Andina, J. J. (2024). Time-Delay Sliding Mode Control for Trajectory Tracking of Robot Manipulators. IEEE Transactions on Industrial Electronics.

[2] Wang, Y., & Wang, B. (2024). Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU. IECE Transactions on Intelligent Systematics, 1(1), 19-29.

[3] Sun, M., Ge, S. S., & Mareels, I. M. (2006). Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning. IEEE Transactions on Robotics, 22(3), 563-568.

[4] Ding, F., Lv, L., Pan, J., Wan, X., & Jin, X. B. (2020). Two-stage gradient-based iterative estimation methods for controlled autoregressive systems using the measurement data. International Journal of Control, Automation and Systems, 18(4), 886-896.

[5] Ma, H., Zhang, X., Liu, Q., Ding, F., Jin, X. B., Alsaedi, A., & Hayat, T. (2020). Partially-coupled gradient-based iterative algorithms for multivariable output-error-like systems with autoregressive moving average noises. IET Control Theory & Applications, 14(17), 2613-2627.

[6] Baek, J., Jin, M., & Han, S. (2016). A new adaptive sliding-mode control scheme for application to robot manipulators. IEEE Transactions on industrial electronics, 63(6), 3628-3637.

[7] Dai, L., Yu, Y., Zhai, D. H., Huang, T., & Xia, Y. (2020). Robust model predictive tracking control for robot manipulators with disturbances. IEEE Transactions on industrial electronics, 68(5), 4288-4297.

[8] Chang, W., Li, Y., & Tong, S. (2018). Adaptive fuzzy backstepping tracking control for flexible robotic manipulator. IEEE/CAA Journal of Automatica Sinica, 8(12), 1923-1930.

[9] Yang, C., Huang, D., He, W., & Cheng, L. (2020). Neural control of robot manipulators with trajectory tracking constraints and input saturation. IEEE Transactions on Neural Networks and Learning Systems, 32(9), 4231-4242.

[10] Jin, X., Sun, T., Chen, W., Ma, H., Wang, Y., & Zheng, Y. (2024). Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM. IECE Transactions on Intelligent Systematics, 1(1), 40-48.

[11] Yu, S., Yu, X., Shirinzadeh, B., & Man, Z. (2005). Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica, 41(11), 1957-1964.

[12] Ren, B., Liu, J., Zhang, S., Yang, C., & Na, J. (2024). On-line Configuration Identification and Control of Modular Reconfigurable Flight Array. IECE Transactions on Intelligent Systematics, 1(2), 91-101.

[13] Zhang, Y., & Hua, C. (2022). Composite learning finite-time control of robotic systems with output constraints. IEEE Transactions on Industrial Electronics, 70(2), 1687-1695.

[14] Gao, M., Ding, L., & Jin, X. (2021). ELM-based adaptive faster fixed-time control of robotic manipulator systems. IEEE Transactions on Neural Networks and Learning Systems, 34(8), 4646-4658.

[15] Xie, Y., Ma, Q., Gu, J., & Zhou, G. (2022). Event-triggered fixed-time practical tracking control for flexible-joint robot. IEEE Transactions on Fuzzy Systems, 31(1), 67-76.

[16] Van, M., Sun, Y., Mcllvanna, S., Nguyen, M. N., Khyam, M. O., & Ceglarek, D. (2023). Adaptive fuzzy fault tolerant control for robot manipulators with fixed-time convergence. IEEE Transactions on Fuzzy Systems, 31(9), 3210-3219.

[17] Sánchez-Torres, J. D., Sanchez, E. N., & Loukianov, A. G. (2015, July). Predefined-time stability of dynamical systems with sliding modes. In 2015 American control conference (ACC) (pp. 5842-5846). IEEE.

[18] Hu, S., Chen, Q., Ren, X., & Wang, S. (2024). Adaptive Predefined-Time Synchronization and Tracking Control for Multimotor Driving Servo Systems. IEEE/ASME Transactions on Mechatronics.

[19] Wang, Q., Cao, J., & Liu, H. (2022). Adaptive fuzzy control of nonlinear systems with predefined time and accuracy. IEEE Transactions on Fuzzy Systems, 30(12), 5152-5165.

[20] Xie, S., & Chen, Q. (2021). Adaptive nonsingular predefined-time control for attitude stabilization of rigid spacecrafts. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(1), 189-193.

[21] Fan, Y., Yang, C., Zhan, H., & Li, Y. (2024). Neuro-Adaptive-Based Predefined-Time Smooth Control for Manipulators With Disturbance. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(8), 4605-4616.

[22] Jia, C., Liu, X., & Xu, J. (2023). Predefined-Time Nonsingular Sliding Mode Control and Its Application to Nonlinear Systems. IEEE Transactions on Industrial Informatics, 20(4), 5829-5837.

[23] Ni, J., & Shi, P. (2020). Global predefined time and accuracy adaptive neural network control for uncertain strict-feedback systems with output constraint and dead zone. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(12), 7903-7918.

[24] Ding, M., Wu, H., Zheng, X., & Guo, Y. (2022). Adaptive predefined-time attitude stabilization control of space continuum robot. IEEE Transactions on Circuits and Systems II: Express Briefs, 71(2), 647-651.

[25] Wu, Y. Y., Liu, W., Zhang, J., Li, X., & Wang, P. (2024). Tunable Predefined-Time Attitude Tracking Control for Rigid Spacecraft. IEEE Transactions on Circuits and Systems II: Express Briefs, 71(9), 4271-4275.

[26] Munoz-Vazquez, A. J., Sánchez-Torres, J. D., Jimenez-Rodriguez, E., & Loukianov, A. G. (2019). Predefined-time robust stabilization of robotic manipulators. IEEE/ASME Transactions on Mechatronics, 24(3), 1033-1040.

[27] Zhou, Q., Zhao, S., Li, H., Lu, R., & Wu, C. (2018). Adaptive neural network tracking control for robotic manipulators with dead zone. IEEE Transactions on Neural Networks and Learning Systems, 30(12), 3611-3620.

[28] Zhu, Y., Qiao, J., & Guo, L. (2018). Adaptive sliding mode disturbance observer-based composite control with prescribed performance of space manipulators for target capturing. IEEE Transactions on Industrial Electronics, 66(3), 1973-1983.

[29] Zou, A. M., Kumar, K. D., & de Ruiter, A. H. (2016). Robust attitude tracking control of spacecraft under control input magnitude and rate saturations. International Journal of Robust and Nonlinear Control, 26(4), 799-815.

[30] Xie, S., Chen, Q., & Yang, Q. (2022). Adaptive fuzzy predefined-time dynamic surface control for attitude tracking of spacecraft with state constraints. IEEE Transactions on Fuzzy Systems, 31(7), 2292-2304.

[31] Chen, Q., Li, Y., Hong, Y., & Shi, H. (2024). Prescribed-Time Robust Repetitive Learning Control for PMSM Servo Systems. IEEE Transactions on Industrial Electronics,71(11), 14753-14763.

[32] Shi, H., Chen, Q., Hong, Y., Ou, X., & He, X. (2024). Adaptive Fuzzy Iterative Learning Control of Constrained Systems With Arbitrary Initial State Errors and Unknown Control Gain. IEEE Transactions on Automation Science and Engineering, 1-12.


Cite This Article
APA Style
Shi, H., Xie, S., Chen, Q., Hu, S., & Yi, S. (2024). Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators. IECE Transactions on Sensing, Communication, and Control, 1(2), 126–135. https://doi.org/10.62762/TSCC.2024.672831

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 51
PDF Downloads: 18

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Sensing, Communication, and Control

IECE Transactions on Sensing, Communication, and Control

ISSN: 3065-7431 (Online) | ISSN: 3065-7423 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.