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Volume 14
Issue 2
10.3390/pr14020278
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network

by
Dongsheng Ma
1,
Li Ren
1,
Tianli Li
1,
Mahmud Iwan Solihin
2 and
Juchen Li
1,2,*
1
School of Intelligent Manufacturing, Anhui Wenda University of Information Engineering, Hefei 231201, China
2
Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia
*
Author to whom correspondence should be addressed.
Processes 2026, 14(2), 278; https://doi.org/10.3390/pr14020278
Submission received: 5 December 2025 / Revised: 6 January 2026 / Accepted: 10 January 2026 / Published: 13 January 2026
(This article belongs to the Section Automation Control Systems)
Versions Notes

Abstract

A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation.
Keywords: fixed-time control; adaptive neural network; robotic manipulator; sliding mode reaching law; path tracking fixed-time control; adaptive neural network; robotic manipulator; sliding mode reaching law; path tracking

Share and Cite

MDPI and ACS Style

Ma, D.; Ren, L.; Li, T.; Solihin, M.I.; Li, J. Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network. Processes 2026, 14, 278. https://doi.org/10.3390/pr14020278

AMA Style

Ma D, Ren L, Li T, Solihin MI, Li J. Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network. Processes. 2026; 14(2):278. https://doi.org/10.3390/pr14020278

Chicago/Turabian Style

Ma, Dongsheng, Li Ren, Tianli Li, Mahmud Iwan Solihin, and Juchen Li. 2026. "Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network" Processes 14, no. 2: 278. https://doi.org/10.3390/pr14020278

APA Style

Ma, D., Ren, L., Li, T., Solihin, M. I., & Li, J. (2026). Fixed-Time Path Tracking Control of Uncertain Robotic Manipulator Based on Adaptive Deviation Correction and Compensation Mechanism Neural Network. Processes, 14(2), 278. https://doi.org/10.3390/pr14020278

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