Robust fractional-order sliding mode control for robotic manipulator system with time-varying disturbances
This paper presents a novel fractional-order sliding mode control (FOSMC) strategy for a robotic manipulator, addressing key limitations of conventional sliding mode control (SMC) methods. While traditional SMC is widely recognized for its robustness against external disturbances and uncertainties,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Franklin Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000775 |
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| Summary: | This paper presents a novel fractional-order sliding mode control (FOSMC) strategy for a robotic manipulator, addressing key limitations of conventional sliding mode control (SMC) methods. While traditional SMC is widely recognized for its robustness against external disturbances and uncertainties, it often suffers from the chattering phenomenon, resulting in energy inefficiency and mechanical wear. The proposed FOSMC leverages the principles of fractional calculus to enable a more flexible and smooth control action, effectively mitigating chattering while maintaining the robustness of SMC. A comprehensive Lyapunov-based analysis confirms the global stability and convergence of the proposed control strategy under both static and dynamic uncertainties. The developed control system explicitly accounts for time-varying disturbances, mechanical friction, and model uncertainties, which are frequently oversimplified in previous studies. To simulate real-world operational scenarios, the system is tested under dynamic disturbances characterized by abrupt or uncertain changes. Comparative simulations are conducted against conventional SMC and other state-of-the-art methods in the literature, demonstrating that the proposed FOSMC outperforms existing approaches. The results show a remarkable performance in trajectory tracking, disturbance rejection, and chattering reduction under realistic operational scenarios. This work thus contributes a practical and theoretically robust control methodology, enabling advanced applications in industrial automation and robotic systems operating in uncertain environments. |
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| ISSN: | 2773-1863 |